Unsupervised learning models of invariant features in images: Recent developments in multistage architecture approach for object detection

Unsupervised learning models of invariant features in images: Recent developments in multistage architecture approach for object detection

Sonia Mittal Nirma University, India

ABSTRACT

Object detection and recognition are important problems in computer vision and pattern recognition domain. Human beings are able to detect and classify objects effortlessly but replication of this ability on computer based systems has proved to be a non-trivial task. In particular, despite significant research efforts focused on meta- heuristic object detection and recognition, robust and reliable object recognition systems in real time remain elusive. Here we present a survey of one particular approach that has proved very promising for invariant feature recognition and which is a key initial stage of multi-stage network architecture methods for the high level task of object recognition

KEYWORDS Unsupervised feature learning, CNNs, Tiled CNNs, Deep learning Original Source URL: http://aircconline.com/ijscai/V5N1/5116ijscai07.pdf http://airccse.org/journal/ijscai/current2016.html

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