Progress IN Computer Recognition Systems

CNN,GAN,Transfer Learning, Data Augmentation/Annotation, Deepfake, YOLO ,Face recognition,object detection,tracking

With their distinctiveness and stability over time, fingerprints continue to be the most widely used anatomical characteristic in systems that automatically recognize a person's identity.

During the past two decades there has been a considerable growth in interest in problems of pattern recognition and image processing (PRIP). This inter­ est has created an increasing need for methods and techniques for the design of PRIP systems. PRIP involves analysis, classification and interpretation of data. Practical applications of PRIP include character recognition, re­ mote sensing, analysis of medical signals and images, fingerprint and face identification, target recognition and speech understanding. One difficulty in making PRIP systems practically feasible, and hence, more popularly used, is the requirement of computer time and storage. This situation is particularly serious when the patterns to be analyzed are quite complex. Thus it is of the utmost importance to investigate special comput­ er architectures and their implementations for PRIP. Since the advent of VLSI technology, it is possible to put thousands of components on one chip. This reduces the cost of processors and increases the processing speed. VLSI algorithms and their implementations have been recently developed for PRIP. This book is intended to document the recent major progress in VLSI system design for PRIP applications.

Although the capabilities of computer image analysis do not yet match those of the human visual system, recent developments have made great progress towards tackling the challenges posed by the perceptual analysis of images.