An approach to image thresholding based on the plausibility of object and background regions by adopting a co-occurrence matrix and category utility is presented. The effectiveness of the proposed method is shown through the experimental results tested on several images and compared with conventional methods.

Various contrast enhancement methods such as histogram equalization (HE) and local contrast enhancement (LCE) have been developed to increase the visibility and details of a degraded image. We propose an image contrast enhancement method based on the global and local adjustment of gray levels by combining HE with LCE methods. For the optimal combination of both, we introduce a discrete entropy. Evaluation of our experimental results shows that the proposed method outperforms both the HE and LCE methods.

We propose a thresholding method based on interval-valued fuzzy sets which are used to define the grade of a gray level belonging to one of the two classes, an object and the background of an image. The effectiveness of the proposed method is demonstrated by comparing our classification results on eight test images to results from the conventional methods.

Time is considered as an important factor in modeling and operation of dynamic systems. However, few studies have considered time factor in modeling and inference of fuzzy cognitive maps (FCMs), besides, no studies have dealt with time delay in learning of FCMs. Therefore, we propose a learning rule for temporal FCMs involving post- and pre-delay time by extending Oja's learning rule. We show the effectiveness of the proposed rule through simulations which solve a time-delayed chemical plant control problem.

The thresholding results for gray level images depend greatly on the thresholding method applied. However, this letter proposes a histogram equalization-based thresholding algorithm that makes the thresholding results insensitive to the thresholding method applied. Experimental results are presented to demonstrate the effectiveness of the proposed thresholding algorithm.

We propose a simple but effective image segmentation method not based on thresholding but on a merging strategy by evaluating joint probability of gray levels on co-occurrence matrix. The effectiveness of the proposed method is shown through a segmentation experiment.

Fuzzy cognitive maps (FCMs) are used to support decision-making, and the decision processes are performed by inference of FCMs. The inference greatly depends on activation functions such as sigmoid function, hyperbolic tangent function, step function, and threshold linear function. However, the sigmoid functions widely used for decision-making processes have been designed by experts. Therefore, we propose a method for designing sigmoid functions through Lyapunov stability analysis. We show the usefulness of the proposed method through the experimental results in inference of FCMs using the designed sigmoid functions.

Histogram equalization is the most popular method for image enhancement. However it has some drawbacks: i) it causes undesirable artifacts and ii) it can degrade the visual quality. To overcome the drawbacks, in this letter, multi-histogram equalization on smoothed histogram using a Gaussian kernel is proposed. To demonstrate the effectiveness, the method is tested on several images and compared with conventional methods.