Competitive stochastic neural networks for Vector Quantization of images

کاهش رنگ تصاویر با شبکه‌های عصبی خودسامانده چندمرحله‌ای و ویژگی‌های افزونه

Reducing the number of colors in an image while preserving its quality, is of importance in many applications such as image analysis and compression. It also decreases memory and transmission bandwidth requirements. Moreover, classification of image colors is applicable in image segmentation and object detection and separation, as well as producing pseudo-color images. In this paper, the Kohene...

Diagnosis of brain tumor using PNN neural networks

Cells grow and then need a very neat method to create new cells that work properly to maintain the health of the body. When the ability to control the growth of the cells is lost, they are unconsidered and often divided without order. Exemplified cells form a tissue mass called the tumor. In fact, brain tumors are abnormal and uncontrolled cell proliferations. Segmentation methods are used in b...

A hierarchical neural network and its application to image segmentation

The problem of image segmentation can be formulated as one of vector quantization. Although self-organizing networks with competitive learning are useful for vector quantization, they, in their original single-layer structure, are inadequate for image segmentation. This paper proposes and describes a hierarchical self-organizing neural network for image segmentation. The hierarchical self-organ...

Image compression using frequency sensitive competitive neural network

Vector Quantization is one of the most powerful techniques used for speech and image compression at medium to low bit rates. Frequency Sensitive Competitive Learning algorithm (FSCL) is particularly effective for adaptive vector quantization in image compression systems. This paper presents a compression scheme for grayscale still images, by using this FSCL method. In this paper, we have genera...

Stochastic Vector Quantization, and Stochastic VQ with State Feedback Using Neural Networks