Mostakim, MoinKamran, Sharif AmitBin Khaled, Md. AsifBin Kabir, Sabit2017-05-092017-05-0920174/19/2017ID 13101176ID 12201105ID 13101194http://hdl.handle.net/10361/8112Cataloged from PDF version of thesis report.Includes bibliographical references (page 31-33).This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.Classification of images has been a widely regarded challenge for the past decade, but a new type of object recognition problem which deals with pixellevel segmentation is posing a more complex task for both computer vision enthusiasts and researcher alike. The convolutional neural network has become a staple for any recognition task, but a new type of ConvNet which is Fully convolutional in architecture has yielded more fine features and proponents. We propose a neural net where we take VGG19 [20], a well-known classification CNN, make it fully convolutional for extracting deeper features and lastly use skip-architectures[15] for getting finer output. This yields better result than the pre-existing FCN segmentation architecture [15, 25, 6]. Training was done on augmented VOC12 [4] with SBD [6]training data and validation set was used from reduced VOC12 validation dataset. The model scored mIOU of 68.1 percent in PASCAL VOC 2012 Segmentation challenge.33 pagesenBRAC University thesis are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Neural networkImage segmentationThesis