دانلود کتاب روش های پیشرفته و یادگیری عمیق در بینایی کامپیوتر

It is now close to a decade since the explosive growth in the development and application of deep neural networks (DNNs) came about, and their subsequent progress has been little short of remarkable. True, this progress has been helped considerably by the deployment of special hardware in the form of powerful GPUs; and their progress followed from the realization that CNNs constituted a crucial architectural base, to which features such as ReLUs, pooling, fully connected layers, unpooling and deconvolution could also be included. In fact, all these techniques helped to breathe life into DNNs and to extend their use dramatically, so the initial near-exponential growth in their use has been maintained without break for the whole subsequent period. Not only has the power of the approach been impressive but its application has widened considerably from the initial emphasis on rapid object location and image segmentation—and even semantic segmentation—to aspects pertaining to video rather than mere image analysis. It would be idle to assert that the whole of the development of computer vision since 2012 has been due solely to the advent of DNNs. Other important techniques such as reinforcement learning, transfer learning, self-supervision, linguistic description of images, label propagation, and applications such as novelty and anomaly detection, image inpainting and tracking have all played a part and contributed to the widening and maturing of computer vision. Nevertheless, many such techniques and application areas have been stimulated, challenged, and enhanced by the extremely rapid take-up of DNNs. It is the purpose of this volume to explore the way computer vision has advanced since these dramatic changes were instigated. Indeed, we can validly ask where we are now, and how solid is the deep neural and machine learning base on which computer vision has recently embarked. Has this been a coherent movement or a blind opportunistic rush forward in which workers have ignored important possibilities, and can we see further into the future and be sure that we are advancing in the right direction? Or is this a case where each worker can take his or her own viewpoint and for any given application merely attend to what appears to be necessary, and if so, is anything lost by employing a limited approach of this sort? In fact, there are other highly pertinent questions to be answered, such as the thorny one of the extent to which a deep network can only be as powerful as the dataset it is trained on; this question will presumably apply to any alternative learning-based approach, whether describable as a DNN or not. Employing reinforcement learning or self-supervision or other approaches will surely not affect this likely limitation. And note that human beings are hardly examples of how extensive training can in any way be avoided; their transfer learning capabilities will be a vital aspect of how efficient the learning process can be made.