Computer Vision
Image processing concerns image properties and image-to-image transformations, whereas the main target of computer vision is the 3-D world. As most computer vision algorithms require some form of image processing, the overlap between the two areas is significant. The contours of the two also blend into those of robotics, signal processing, pattern recognition, control theory, artificial intelligence and other fields.
As a scientific discipline, computer vision is concerned with the theory and technology for building artificial systems that obtain information from images or multi-dimensional data.A significant part of artificial intelligence deals with planning or deliberation for system which can perform mechanical actions such as moving a robot through some environment.This type of processing typically needs input data provided by a computer vision system, acting as a vision sensor and providing high-level information about the environment and the robot.

Feature detection methods based on the combination of Gaussian derivative operators at multiple scales. Special focus is given to the problem of scale selection, in order to adapt the local scales of processing to the local image structure. Specifically, the notion of automatic scale selection based on gamma-normalized derivatives makes it possible to define scale-invariant image features. The use of such scale-invariant image features allows the vision system to automatically handle the unknown scale variations that may occur in real-world image data, due to objects of different physical size as well as objects with different distances to the camera.
the journal itself is changing: As our field evolves, so does the scope of IJCV, now embracing both the foundational aspects of computer vision—such as image formation, sensing, processing, analysis, and interpretation, and a number of striving application domains—such as image-based rendering, image retrieval, medical imaging, surveillance, and video analysis and annotation.