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44                    <a href="#" class="navbar-brand scroll-top logo"><b>Mohamed Helala</b></a>
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48                    <ul class="nav navbar-nav" id="mainNav">
49                        <li class="active"><a href="#home" class="scroll-link">Home</a></li>
50                        <li><a href="#aboutMe" class="scroll-link">About Me</a></li>
51                        <li><a href="#projects" class="scroll-link">Projects</a></li>
52                        <li><a href="#publications" class="scroll-link">Publications</a></li>
53			<li><a href="#activities-awards" class="scroll-link">Activities & Awards</a></li>
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64	<div id='snav' class='en'>
65	  <ul>
66	    <li> <a href='http://mhelala.blogspot.ca/'> <i class="fa fa-bold"></i> <span>Blog</span> </a> </li>
67	    <li> <a href='https://twitter.com/Mohamed_Helala'> <i class="fa fa-twitter"></i> <span>Twitter</span> </a> </li>
68	    <li> <a href='https://github.com/mohamed-helala'> <i class="fa fa-github"></i> <span>Git</span> </a> </li>
69	    <li> <a href='https://plus.google.com/114765445236633498280/posts'> <i class="fa fa-google-plus"></i> <span>Google Plus</span> </a> </li>
70	    <li> <a href='http://www.linkedin.com/pub/mohamed-helala/71/503/681'> <i class="fa fa-linkedin"></i> <span>Linkedin</span> </a> </li>
71	    <li> <a href='http://scholar.google.ca/citations?user=Dw5bcNQAAAAJ&hl=en'> <i class="fa fa-book"></i> <span>Google Scholar</span> </a> </li>
72	  </ul>
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75    <section id="home">
76        <div class="banner-container">
77            <!--<img src="images/banner-bg.jpg" alt="banner" />-->
78	    <div class="empty-container"></div>
79            <div class="container banner-content">
80                <div id="da-slider" class="da-slider">
81                    <div class="da-slide">
82            			<h2>Scalable Vision Pipelines</h2>
83            			<p><a href="javascript:customScripts.scroll('#stream')">A formal stream algebra for efficient scalable computer vision pipelines.
84            			</a></p>
85                        <div class="da-img"> <img src="img/stream.png" alt="stream" /></div>
86                    </div>
87                    <div class="da-slide">
88                        <h2>Image Retrieval</h2>
89                        <p><a href="http://mytomcatapp-helala.rhcloud.com/ImageSearch/">Try our demo for content based image retrieval.</a></p>
90			             <div class="da-img"> <img src="img/image-ret.png" alt="image retrieval demo" /></div>
91                    </div>
92                    <div class="da-slide">
93                        <h2>Traffic Video Survillance</h2>
94                        <p><a href="javascript:customScripts.scroll('#traffic')">Automatic road and lane boundary detection.</a></p>
95                        <div class="da-img"> <img src="img/traffic.png" alt="traffic" /></div>
96                    </div>
97                    <div class="da-slide">
98                        <h2>Image Correspondance</h2>
99                        <p><a href="javascript:customScripts.scroll('#stereo')">Developing cost subvolume filtering for solving pixel labeling problems</a></p>
100                        <div class="da-img"> <img src="img/imcoress.png" alt="traffic" /></div>
101                    </div>
102		            <div class="da-slide">
103                        <h2>Image Mosaicking</h2>
104                        <p><a href="javascript:customScripts.scroll('#mosaics')">Multi-view mosaics from near ground aerial imagery</a></p>
105                        <div class="da-img"> <img src="img/mosaic.png" alt="traffic" /></div>
106                    </div>
107				<!--  <nav class="da-arrows">
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111                </div>
112            </div>
113        </div>
114    </section>
115    <section id="introText">
116            <div class="parlex-back">
117            <div class=" text-center">
118				<h2>There are no negatives in life, only challenges to overcome that will make you stronger.</h2><h3>-Eric Bates</h3>
119            </div>
120            <!--/.container-->
121        </div>
122
123    </section>
124    <!--About-->
125    <section id="aboutMe" class="secPad titlebox">
126        <div class="container">
127            <div class="row">
128		<!-- item -->
129                <div class="col-md-3 text-center tileBox">
130                    <div class="txtHead">
131                    <h3><i class="fa fa-edit"></i><span class="id-color">Contact</span></h3></div>
132			<p>Mohamed A. Helala<br>
133			PhD Student in Computer Science, Faculty of Science, UOIT<br>
134			<abbr title="Phone">P:</abbr> (905) 721-8668 <abbr title="extention">ext:</abbr> 2387<br>
135			<a href="mailto:#">mohamed.helala "at" uoit "dot" ca</a></p>
136                </div>
137                <!-- end: -->
138		
139                <!-- item -->
140                <div class="col-md-3 text-center tileBox">
141                   <div class="txtHead">
142                    <h3><i class="fa fa-gears"></i> <span class="id-color">Industry</span></h3></div>
143                    <p class="text-left">(2007-2011) A Software engineer in <a href="http://www.hoiaste.com/index.php"><span>HOI/Aste</span></a> R&D department. I developed a DXF engine for generating Autocad files and a software system for automatic generation of drawings.</p>
144                </div>
145                <!-- end: -->
146
147                <!-- item -->
148                <div class="col-md-3 text-center tileBox">
149                    <div class="txtHead">
150                    <h3><i class="fa fa-lightbulb-o"></i><span class="id-color">Research</span></h3></div>
151                    <p class="text-left">(2011-now) A PhD Student in the UOIT computer vision group with <a href="http://faculty.uoit.ca/qureshi/"><span>Dr. Fasial Qureshi</span></a> and <a href="http://leda.science.uoit.ca/kenpu/"><span>Dr. Ken Pu</span></a>. My interests lie in computer vision and data stream processing.</p>
152                </div>
153                <!-- end: -->
154
155                <!-- item -->
156                <div class="col-md-3 text-center tileBox">
157                    <div class="txtHead">
158                    <h3><i class="fa fa-edit"></i><span class="id-color">Teaching</span></h3></div>
159                    <p>I worked as TA in UOIT for several courses such as <a href="http://leda.science.uoit.ca/teaching/compiler/index">compilers</a>, <a href="http://leda.science.uoit.ca/teaching/pl/index">programming languages</a>, <a href="http://leda.science.uoit.ca/teaching/sysdev/index">system development and Integration</a>, and software design and analysis.</p>
160                </div>
161                <!-- end: -->
162
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164	    
165		<div class="container">
166			<div class="row">
167				<div id="nt-example2-container" class="col-md-4 centered nt-example2-face">
168					<ul id="nt-example2">
169					    <li data-infos="I attended ACCV 2014 in NUS, Singapore and presented the paper 'Accelerating Cost Volume Filtering Using Salient Subvolumes and Robust Occlusion Handling'. This paper presents our Accelerated Cost Volume Filtering (ACF) method, which shows speedup over the traditional Cost Volume Filtering (CF) method.">
170						<i class="fa fa-fw fa-play state"></i>
171						 <span imgdata="img/accv14.png"></span>Poster in ACCV 2014.
172					    </li>
173					    <li data-infos="I presented the paper 'Towards Efficient Feedback Control in Streaming Computer Vision Pipelines' in the UCCV 2014 workshop (co-located with ACCV 2014). This paper shows that our stream algebra framework can naturally describe feedback control in computer vision pipelines.">
174						<i class="fa fa-fw fa-play state"></i>
175						 <span imgdata="img/uccv14.png"></span>Presentation in UCCV 2014.
176					    </li>
177					    <li data-infos="The paper 'A Stream Algebra for Computer Vision Pipelines is presented in the VSM 2014 workshop (co-located with CVPR 2014). This paper presents the first formal stream algebra that provides a mathematical description of vision pipelines and depicts the distributed manipulation of image and video streams.">
178						<i class="fa fa-fw fa-play state"></i>
179						 <span imgdata="img/vsm14.png"></span>Presentation in VSM 2014.
180					    </li>
181					    <li data-infos="I released an online demo for Content Based Image Retrieval (CBIR). This demo uses keyword search to retrieve a relevant set of images, then user can select a query image and a set of features to re-rank results based on similarity.">
182						<i class="fa fa-fw fa-play state"></i>
183						 <span imgdata="img/cbirsearch.png">CBIR Online Demo.
184					    </li>
185					</ul>
186					<div class="nt-example2-face nt-example2-panel centered">
187						<i class="fa fa-arrow-left" id="nt-example2-prev"></i>
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190				</div>
191				<div id="nt-example2-infos-container" class = "col-md-8 nt-example2-face">
192					<div id="nt-example2-infos-triangle"></div>
193					<div id="nt-example2-infos" class="row">
194						<div class="col-xs-4 centered">
195							<img src="img/accv14.png" class="infos-image">
196						</div>
197						<div class="col-xs-8">
198							<div class="infos-text"> I attended ACCV 2014 in NUS, Singapore and presented the paper 'Accelerating Cost Volume Filtering Using Salient Subvolumes and Robust Occlusion Handling'. This paper presents our Accelerated Cost Volume Filtering (ACF) method, which shows speedup over the traditional Cost Volume Filtering (CF) method. </div>
199						</div>
200					</div>
201				</div>
202			</div>
203		</div>
204	
205        </div>
206    </section>
207    <!--Quote-->
208    <section id="quote" class="bg-parlex">
209        <div class="parlex-back">
210            <div class="container text-center">
211				<h2>Research is formalized curiosity. It is poking and prying with a purpose.</h2><h3>-Zora Neale Hurston</h3>
212            </div>.
213
214            <!--/.container-->
215        </div>
216    </section> 
217    <!--Experience-->
218    <section id="projects" class="secPad">
219    	<div class="container">     
220           <div class="heading text-center">
221                <!-- Heading -->
222                <h2>Professional Activities</h2>
223                <p>A timeline for my research activities and developed projects.</p>
224            </div>
225            <div id="timeline">
226                <div class="row timeline-movement timeline-movement-top">
227                    <div class="timeline-badge timeline-future-movement">
228                        <a href="#">
229                            <span class="glyphicon glyphicon-plus"></span>
230                        </a>
231                    </div>
232                    <div class="timeline-badge timeline-filter-movement">
233                        <a href="#">
234                            <span class="glyphicon glyphicon-time"></span>
235                        </a>
236                    </div>
237        
238                </div>
239		<div id="dyn-rec" class="row timeline-movement">
240    
241                    <div  class="timeline-badge">
242                        <span class="timeline-balloon-date-day">Jan</span>
243                        <span class="timeline-balloon-date-month">2015</span>
244                    </div>
245    
246    
247                    <div class="col-sm-6  timeline-item">
248                        <div class="row">
249                            <div class="col-sm-11">
250                                <div class="timeline-panel credits">
251                                    <ul class="timeline-panel-ul">
252                                        <li><span class="importo">Demo</span></li>
253                                        <li><span class="causale"> 
254			    <li><span class="causale"> 
255                            <div class="embed-responsive embed-responsive-16by9">
256                                <iframe class="embed-responsive-item" src="https://www.youtube.com/embed/eUPf2FJK3ck" allowfullscreen></iframe>
257                            </div></span></li>
258			    <li class="text-left"><span>Example of running our traffic pipeline on an input video sequence. The pipeline copies the input stream into different stream processing branches. The top view window is for the output branch and the bottom one is for the edges detected by the edge clustering branch. The chart displays the average latency (in red) and period (in green, throughput = 1/period) for the edge clustering branch. The latency is minimized dynamically at runtime by applying load balancing on the clustering branch. Stop the video before and after 00:17 (load balancing) to see the delay between the two branches (difference between the two frame numbers shown in red). </span> </li>
259                                        <li><p><small class="text-muted"> </small></p> </li>
260                                    </ul>
261                                </div>
262                
263                            </div>
264                        </div>
265                    </div>
266    
267                    <div class="col-sm-6  timeline-item">
268                        <div class="row">
269                            <div class="col-sm-offset-1 col-sm-11">
270                                <div class="timeline-panel debits">
271                                    <ul class="timeline-panel-ul">
272                                        <li><span class="importo">Dynamic Reconfiguration of Streaming Computer Vision Pipelines</span></li>
273                                        <li><span class="causale"> We developed a framework for our formal stream algebra and implemented dynamic reconfiguration to select the best execution plan for a given computer vision pipeline. This is currently accomplished by applying load balancing on a linear pipeline of tasks to minimize the pipeline latency at a predefined value of throughput.</span> </li>
274                                        <a href="javascript:customScripts.scroll('#cvpr14')">[Reference 1]</a>
275					<a href="javascript:customScripts.scroll('#avss12')">[Reference 2]</a>
276                                        <li><p><small class="text-muted"> 1/1/2015 - 1/03/2015</small></p> </li>
277                                    </ul>
278                                </div>
279                            </div>
280                        </div>
281                    </div>
282                </div>
283		<div id="feedback-ctrl" class="row timeline-movement">
284    
285                    <div  class="timeline-badge">
286                        <span class="timeline-balloon-date-day">May</span>
287                        <span class="timeline-balloon-date-month">2014</span>
288                    </div>
289    
290    
291                    <div class="col-sm-6  timeline-item">
292                        <div class="row">
293                            <div class="col-sm-11">
294                                <div class="timeline-panel credits">
295                                    <ul class="timeline-panel-ul">
296                                        <li><span class="importo">Demo</span></li>
297                                        <li><span class="causale"> 
298			    <li><span class="causale"> 
299                            <div class="embed-responsive embed-responsive-16by9">
300                                <iframe class="embed-responsive-item" src="https://www.youtube.com/embed/gDfvdXNgozI" allowfullscreen></iframe>
301                            </div></span></li>
302			    <li class="text-left"><span>We copy an input traffic video stream into two streaming pipeline branches. The first branch contains a SIFT keypoint extraction task to generate a set of SIFT keypoints for each frame. The second branch outputs a list of edges for each frame using an edge detection task. We define for each task, a threshold to control the number of output kepoints and edges. 
303			    A manual change is performed to each thresold and feedback control will automatically tune the thresold value to maintain a predifined output number of edges/points.</span> </li>
304                                        <li><p><small class="text-muted"> UCCV 2014</small></p> </li>
305                                    </ul>
306                                </div>
307                
308                            </div>
309                        </div>
310                    </div>
311    
312                    <div class="col-sm-6  timeline-item">
313                        <div class="row">
314                            <div class="col-sm-offset-1 col-sm-11">
315                                <div class="timeline-panel debits">
316                                    <ul class="timeline-panel-ul">
317                                        <li><span class="importo">Feedback Control in Streaming Computer Vision Pipelines</span></li>
318                                        <li><span class="causale"> Stream processing is currently an active research direction in
319  computer vision. This is due to the existence of many computer
320  vision algorithms that can be expressed as a pipeline of operations,
321  and the increasing demand for online systems that process image and
322  video streams. Recently, a formal stream algebra has been proposed
323  as an abstract framework that mathematically describes computer
324  vision pipelines. The algebra defines a set of concurrent operators
325  that can describe a pipeline of vision tasks, with image and video
326  streams as operands. In this paper, we extend this algebra framework
327  by developing a formal and abstract description of feedback control
328  in computer vision pipelines. Feedback control allows vision
329  pipelines to perform adaptive parameter selection, iterative
330  optimization and performance tuning. We show how our extension can
331  describe feedback control in the vision pipelines of two
332  state-of-the-art techniques.</span> </li>
333                                        <a href="javascript:customScripts.scroll('#uccv14')">[Reference]</a>
334                                        <li><p><small class="text-muted"> 15/5/2014 - 1/06/2014</small></p> </li>
335                                    </ul>
336                                </div>
337                            </div>
338                        </div>
339                    </div>
340                </div>
341                <div id="stereo" class="row timeline-movement">
342                    <div class="timeline-badge">
343                        <span class="timeline-balloon-date-day">Feb</span>
344                        <span class="timeline-balloon-date-month">2014</span>
345                    </div>
346
347                    <div class="col-sm-6  timeline-item">
348                        <div class="row">
349                            <div class="col-sm-11">
350                                <div class="timeline-panel credits">
351                                    <ul class="timeline-panel-ul">
352                                        <li><span class="importo">Supplementary Video</span></li>
353                                        <li><span class="causale"> 
354                            <div class="embed-responsive embed-responsive-16by9">
355                                <iframe class="embed-responsive-item" src="http://www.youtube.com/embed/SxcOc7Y22GA" allowfullscreen></iframe>
356                            </div></span></li>
357                                        <li><p><small class="text-muted"> ACCV 2014</small></p> </li>
358                                    </ul>
359                                </div>
360                
361                            </div>
362                        </div>
363                    </div>
364    
365                    <div class="col-sm-6  timeline-item">
366                        <div class="row">
367                            <div class="col-sm-offset-1 col-sm-11">
368                                <div class="timeline-panel debits">
369                                    <ul class="timeline-panel-ul">
370                                        <li><span class="importo">Stereo Correspondence</span></li>
371                                        <li><span class="causale">We propose the Accelerated Cost Volume Filtering (ACF) method, which speeds up the traditional Cost Volume Filtering (CF) method. ACf identifies salient subvolumes in the cost volume. Filtering is restricted to these subvolumes, resulting in significant performance gains. We applied our method to disparity estimation from a stereo image pair and developed an occlusion handling method, which acts as a post-processing step that refines the disparity maps computed via filtering. Currently, we are exploring the use of slanted surfaces during the detection of salient subvolumes and we are implementing an efficie
371nt version of ACF for optical flow computation. </span> </li>
372                                        <a href="javascript:customScripts.scroll('#accv14')">[Reference]</a>
373                                        <li><p><small class="text-muted"> 18/02/2014 - 30/05/2014</small></p> </li>
374                                    </ul>
375                                </div>
376    
377                            </div>
378                        </div>
379                    </div>
380                </div>
381                <div id="stream" class="row timeline-movement">
382    
383                    <div  class="timeline-badge">
384                        <span class="timeline-balloon-date-day">Jan</span>
385                        <span class="timeline-balloon-date-month">2013</span>
386                    </div>
387    
388    
389                    <div class="col-sm-6  timeline-item">
390                        <div class="row">
391                            <div class="col-sm-11">
392                                <div class="timeline-panel credits">
393                                    <ul class="timeline-panel-ul">
394                                        <li><span class="importo">Implementing a Traffic Analysis Pipeline</span></li>
395                                        <li><span class="causale"><img src="img/traffic-pip.png" class="img-responsive"></span> </li>
396                                        <li><p><small class="text-muted"> 1/06/2013 - Present</small></p> </li>
397                                    </ul>
398                                </div>
399                
400                            </div>
401                        </div>
402                    </div>
403    
404                    <div class="col-sm-6  timeline-item">
405                        <div class="row">
406                            <div class="col-sm-offset-1 col-sm-11">
407                                <div class="timeline-panel debits">
408                                    <ul class="timeline-panel-ul">
409                                        <li><span class="importo">Stream Algebra Framework</span></li>
410                                        <li><span class="causale"> We propose a formal stream algebra as an abstract framework that mathematically describes computer vision pipelines. The algebra defines a set of concurrent operators that can describe a pipeline of vision tasks, with image and video streams as operands. We also extend the algebra framework by developing a formal and abstract description of feedback control in computer vision pipelines. Currently, we are studying feedback control to perform adaptive parameter selection, iterative optimization and performance tuning.</span> </li>
411                                        <a href="javascript:customScripts.scroll('#cvpr14')">[Reference]</a>
412                                        <li><p><small class="text-muted"> 1/06/2013 - Present</small></p> </li>
413                                    </ul>
414                                </div>
415                            </div>
416                        </div>
417                    </div>
418                </div>
419    
420                <!--due -->
421                <div id="mosaics" class="row timeline-movement">
422                    <div class="timeline-badge">
423                        <span class="timeline-balloon-date-day">May</span>
424                        <span class="timeline-balloon-date-month">2012</span>
425                    </div>
426                
427                    <div class="col-sm-6  timeline-item">
428                        <div class="row">
429                            <div class="col-sm-11">
430                                <div class="timeline-panel credits">
431                                    <ul class="timeline-panel-ul">
432                                        <li><span class="importo">Supplementary Video</span></li>
433                                        <li><span class="causale">
434                            <div class="embed-responsive embed-responsive-16by9">
435                              <iframe class="embed-responsive-item" src="http://www.youtube.com/embed/qZaElUp0Pc4" allowfullscreen></iframe>
436                            </div> </span> </li>
437                                        <li><p><small class="text-muted">
437Creating view dependent mosaics</small></p> </li>
438                                    </ul>
439                                </div>
440                
441                            </div>
442                        </div>
443                    </div>
444 
445                    <div class="col-sm-6  timeline-item">
446                        <div class="row">
447                            <div class="col-sm-offset-1 col-sm-11">
448                                <div class="timeline-panel debits">
449                                    <ul class="timeline-panel-ul">
450                                        <li><span class="importo">Video Mosaicing</span></li>
451                                        <li><span class="causale">We are developing new techniques for generating large-scale mosaics from near ground UAV videos. Such techniques should be able to handle several challenges such as the parallax effects of buildings toward camera motion, the generation of high-quality mosaics and the support of different camera motion models.</span> </li>
452                                        <a href="javascript:customScripts.scroll('#icdsc12')">[Reference]</a>
453                                        <li><p><small class="text-muted"> 1/05/2012 - 1/09/2012</small></p> </li>
454                                    </ul>
455                                </div>
456                
457                            </div>
458                        </div>
459                    </div>
460                </div>
461                <div id="traffic" class="row timeline-movement">
462                    <div class="timeline-badge">
463                        <span class="timeline-balloon-date-day">Jan</span>
464                        <span class="timeline-balloon-date-month">2012</span>
465                    </div>
466    
467            	    <div class="col-sm-6  timeline-item">
468                        <div class="row">
469                            <div class="col-sm-11">
470                                <div class="timeline-panel credits">
471                                    <ul class="timeline-panel-ul">
472                                        <li><span class="importo">Sample Results</span></li>
473                                        <li><span class="causale"><img src="img/road-detection.png" class="img-responsive"></span> </li>
474                                        <li><p><small class="text-muted">Road boundary detection using our traffic video analysis pipeline.</small></p> </li>
475                                    </ul>
476                                </div>
477                
478                            </div>
479                        </div>
480                    </div>
481 
482                    <div class="col-sm-6  timeline-item">
483                        <div class="row">
484                            <div class="col-sm-offset-1 col-sm-11">
485                                <div class="timeline-panel debits">
486                                    <ul class="timeline-panel-ul">
487                                        <li><span class="importo">Traffic Survillance</span></li>
488                                        <li><span class="causale">We are studying automatic techniques for analyzing traffic video under challenging environmental conditions. We addressed the problem of automatic road and lane boundary detection under severe vision scenarios, such as partially occluded roads and unlit highways. Currently, we are addressing the problems of measuring accurate traffic statistics, identify ongoing and incoming lanes, dynamic camera calibration, and adaption of view changes due to wind or human operators.</span> </li>
489                                        <a href="javascript:customScripts.scroll('#avss12')">[Reference]</a>
490                                        <li><p><small class="text-muted"> 1/01/2012 - 30/05/2012 </small></p> </li>
491                                    </ul>
492                                </div>
493                
494                            </div>
495                        </div>
496                    </div>
497                </div>
498                <div id="cbir" class="row timeline-movement">
499                    <div class="timeline-badge">
500                        <span class="timeline-balloon-date-day">Jan</span>
501                        <span class="timeline-balloon-date-month">2010</span>
502                    </div>
503                	<div class="col-sm-6  timeline-item">
504                        <div class="row">
505                            <div class="col-sm-11">
506                                <div class="timeline-panel credits">
507                                    <ul class="timeline-panel-ul">
508                                        <li><span class="importo">Online Demonstration</span></li>
509                                        <li><span class="causale"><a href="http://mytomcatapp-helala.rhcloud.com/ImageSearch"><img src="img/image-ret.png" class="img-responsive"></a><a href="http://mytomcatapp-helala.rhcloud.com/ImageSearch">Try it now</a></span> </li>
510                                        <li><p><small class="text-muted">Re-ranking keyword search results using a query image.</small></p> </li>
511                                    </ul>
512                                </div>
513                
514                            </div>
515                        </div>
516                    </div>
517 
518                    <div class="col-sm-6  timeline-item">
519                        <div class="row">
520                            <div class="col-sm-offset-1 col-sm-11">
521                                <div class="timeline-panel debits">
522                                    <ul class="timeline-panel-ul">
523                                        <li><span class="importo">Content Based Image Retrieval (CBIR)</span></li>
524                                        <li><span class="causale">During my master work, I studied CBIR, where I addressed broad domain image search and retrieval in large image collections by evaluating recent approaches and developing new techniques to provide more precise image description and better retrieval accuracy.</span> </li>
525                                        <a href="javascript:customScripts.scroll('#ijcsi12')">[Reference]</a>
526                                        <li><p><small class="text-muted"> 1/01/2010 - 1/11/2010</small></p> </li>
527                                    </ul>
528                                </div>
529                
530                            </div>
531                        </div>
532                    </div>
533                </div>
534            </div>
535         </div>
536    </section>
537    
538    <!--Contact -->
539    <section id="publications" class="secPad">
540        <div class="container">
541		<div class="heading text-center">
542                <!-- Heading -->
543                <h2>Publications</h2>
544            </div>
545            <div class="row mrgn30 ref-panel">
546		<div class="list2">
547		   <ol>
548		   <li><p>
549				<a href="#" id="icdsc16" class="papertitle">"Fast Estimation of Large Displacement Optical Flow Using Dominant Motion Patterns & Sub-Volume PatchMatch Filtering."</a>, 
550				<span class="paperauth">Mohamed A. Helala</span>, 
551				Faisal Z. Qureshi, 
552				<span class="papervenue">14th Conference on Computer and Robot Vision (CRV)</span>, 
553				Edmonton, Alberta, Canada, May 2017 (Best Paper Award).
554			</p></li>
555		   <li><p>
556				<a href="http://vclab.ca/wp-content/papercite-data/pdf/16-icdsc-c.pdf" id="icdsc16" class="papertitle">"A Formal Algebra Implementation for Distributed Image and Video Stream Processing."</a>, 
557				<span class="paperauth">Mohamed A. Helala</span>, 
558				Ken Q. Pu, Faisal Z. Qureshi, 
559				<span class="papervenue">Proc.10th ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC)</span>, 
560				Paris, France, September 2016.
561			</p></li>
562			<li><p>
563				<a href="http://vclab.ca/wp-content/papercite-data/pdf/16-wacv-2-c.pdf" id="wacv16" class="papertitle">"Constructing Image Mosaics Using Focus Based Depth Analysis."</a>, 
564				<span class="paperauth">Mohamed A. Helala</span>, 
565				Faisal Z. Qureshi, 
566				<span class="papervenue">IEEE Winter Conference on Applications of Computer Vision (WACV)</span>, 
567				Lake Placid, NY, USA, March 2016.
568			</p></li>
569			<li><p>
570				<a href="http://vclab.ca/wp-content/papercite-data/pdf/15-jei-j.pdf" id="spie15" class="papertitle">"Automatic Parsing of Lane and Road Boundaries in Challenging
571  Traffic Scenes."</a>, 
572				<span class="paperauth">Mohamed A. Helala</span>, 
573				Faisal Z. Qureshi, Ken Q. Pu, 
574				<span class="papervenue">SPIE Journal of Electronic Imaging</span>, 
575				2015.
576			</p></li>
577		      <li><p>
578				<a href="http://vclab.ca/wp-content/papercite-data/pdf/14-uccv-w.pdf" id="uccv14" class="papertitle">"Towards Efficient Feedback Control in Streaming Computer Vision Pipelines."</a>, 
579				<span class="paperauth">Mohamed A. Helala</span>, 
580				Ken Q. Pu, Faisal Z. Qureshi, 
581				<span class="papervenue">2nd Workshop on User-Centered Computer Vision (UCCV) in conjunction with ACCV 2014</span>, 
582				Singapore, Nov. 2014.
583			</p></li>
584		      <li><p><a href="http://vclab.ca/wp-content/papercite-data/pdf/14-accv-c.pdf" id="accv14" class="papertitle">"Accelerating Cost Volume Filtering Using Salient Subvolumes and Robust Occlusion Handling."</a>, 
585				<span class="paperauth">Mohamed A. Helala</span>, 
586				Faisal Z. Qureshi, 
587				<span class="papervenue">12th Asian Conference on Computer Vision (ACCV)</span>, 
588				Singapore, Nov. 2014.
589			</p></li>
590		      <li><p><a href="http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2014/W20/papers/Helala_A_Stream_Algebra_2014_CVPR_paper.pdf" id="cvpr14" class="papertitle">"A Stream Algebra For Computer Vision Pipelines."</a>, 
591				<span class="paperauth">Mohamed A. Helala</span>, 
592				Ken Q. Pu, Faisal Z. Qureshi, 
593				<span class="papervenue">2nd Workshop on Web-scale Vision and Social Media (VSM) in conjunction with CVPR 2014</span>, 
594				Columbus, Ohio, June. 2014.
595			</p></li>
596		      <li><p><a href="files/12-icdsc-c.pdf" id="icdsc12" class="papertitle">"Mosaic of Near Ground UAV Videos Under Parallax Effects"</a>, 
597				<span class="paperauth">Mohamed A. Helala</span>, 
598				Luis A. Zarrabeitia, Faisal Z. Qureshi, 
599				<span class="papervenue">Proc. 6th ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC)</span>, 
600				Hong Kong, China, Oct. 2012.
601			</p></li>
602			<li><p><a href="files/12-avss-c.pdf" id="avss12" class="papertitle">"Road Boundary Detection in Challenging Scenarios"</a>, 
603				<span class="paperauth">Mohamed A. Helala</span>, 
604				 Ken Q. Pu, Faisal Z. Qureshi, 
605				<span class="papervenue">Proc. 9th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS)
606				</span>, Beijing, China, Sept. 2012.
607			</p></li>
608			<li><p><a href="files/master-thesis.pdf" id="master10" class="papertitle">"Quantitative image search based on feature integration"</a>, 
609				<span class="paperauth">Mohamed A. Helala</span>
610				<span class="papervenue">Master Thesis, Benha University
611				</span>, Cairo, Egypt, Dec. 2010.
612			</p></li>
613			<li><p><a href="http://www.ijcsi.org/papers/IJCSI-9-4-1-204-213.pdf" id="ijcsi12" class="papertitle">"A Content Based Image Retrieval Approach Based On Principal Regions Detection"</a>, 
614				<span class="paperauth">Mohamed A. Helala</span>, 
615				 Mazen M. Selim, Hala H. Zayed,
616				<span class="papervenue">International Journal of Computer Science Issues (IJCSI)</span>, 
617				 Vol. 9, Issue 4, No 1, Jul. 2012.
618			</p></li>
619			<li><p><a href="http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5380453" id="iccee12" class="papertitle">"An Image Retrieval Approach Based on Composite Features and Graph Matching"</a>, 
620				<span class="paperauth">Mohamed A. Helala</span>, 
621				 Mazen M. Selim, Hala H. Zayed,
622				<span class="papervenue">Proc. IEEE International Conference on Computer and Electrical Engineering (ICCEE)</span>, 
623				 Dubai, Emirate, Dec. 2009.
624			</p></li>
625		   </ol>
626		</div>
627            </div>
628        </div>
629        <!--/.container-->
630    </section>
631    
632     <section id="activities-awards" class="secPad titlebox">
633        <div class="container">
634            <div class="row">
635		<!-- item -->
636                <div class="col-md-6 text-center tileBox">
637                    <div class="txtHead">
638                    <h3><i class="fa fa-pencil"></i><span class="id-color">Activities</span></h3></div>
639			<ol class="list3 content">
640			<li><p><em>Discussion Group</em>A weekly seminar where all students in the <a href="http://vclab.science.uoit.ca/">visual computing 
641							    	lab</a> discuss recent research in computer vision and graphics.</p></li>
642			<li><p><em>Blog</em><a href="http://mhelala.blogspot.ca/">My Personal Blog</a></p></li>
643			<li><p><em>Reading</em>Currently, I'm reading the book "Decision Forests for Computer Vision and Medical Image Analysis". This book dives into the theory and applications of random forests in computer vision. I like playing with the Sherwood library that accompanies the book.
644            </p></li>
645			</ol>
646                </div>
647                <!-- end: -->
648		
649                <!-- item -->
650                <div class="col-md-6 text-center tileBox">
651                   <div class="txtHead">
652			<h3><i class="fa fa-certificate"></i> <span class="id-color">Awards</span></h3></div>
653			<ol class="list3 content">
654			<li><p><em>2017</em>Best Paper Award - Canadian Conference on Computer and Robot Vision (CRV)</p></li>
655			<li><p><em>2015</em>
655Honorable Mention in IBM HackStreams Challenge</p></li>
656			<li><p><em>2011</em>Awarded the <a href="http://gradstudies.uoit.ca/current_students/student_finances/graduate_student_funding/external_awards/ontario-trillium-scholarship.php">Ontario Trillium Scholarship (OTS) Award</a> </p></li>
657			<li><p><em>2011</em>Nominated for Fulbright scholarship by the binational Fulbright commission in Egypt(only 10 nominations in all fields each year)[Left for commitment with the OTS award]</p></li>
658            <li><p><em>2011</em>Egyptian Government PhD Scholarship Award [Left for commitment with the OTS award]</p></li>
659			<li><p><em>2004</em>The Honor Degree for Academic Distinction</p></li>
660			<li><p><em>2004</em>Dean’s Gold Medal for Graduating First Place in B.Sc. Electrical and Computer Engineering
661</p></li>
662			</ol>
663                </div>
664                <!-- end: -->
665            </div>
666        </div>
667    </section>
668    
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670        <div class="container">
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