Object Recognition on Cotton Harvesting Robot Using Human Visual System

Part 1: Decision Support Systems, Intelligent Systems and Artificial Intelligence ApplicationsInternational audienceObject recognition is one of the hottest issues in the field of vision system for harvesting robot. How efficiently and accurately to remove the background and get the object in image is the key research. The attention mechanisms of human visual system (HVS) can be segmented an image into the region of interesting (ROI) which is considered important and the background which is less important, and recognized the object from ROI using the local information. In this paper, an algorithm based on the characteristic of HVS is proposed. In algorithm, the image was partitioned into many blocks of equal size. ROI was got through calculating the factor of weight of each sub-block image, and the object was extracted by segmenting the ROI. Experiment results show that the algorithm can be recognized the object efficiently and accurately. A new method for vision system of harvesting robot is provided

Image fusion-based contrast enhancement

The goal of contrast enhancement is to improve visibility of image details without introducing unrealistic visual appearances and/or unwanted artefacts. While global contrast-enhancement techniques enhance the overall contrast, their dependences on the global content of the image limit their ability to enhance local details. They also result in significant change in image brightness and introduce saturation artefacts. Local enhancement methods, on the other hand, improve image details but can produce block discontinuities, noise amplification and unnatural image modifications. To remedy these shortcomings, this article presents a fusion-based contrast-enhancement technique which integrates information to overcome the limitations of different contrast-enhancement algorithms. The proposed method balances the requirement of local and global contrast enhancements and a faithful representation of the original image appearance, an objective that is difficult to achieve using traditional enhancement methods. Fusion is performed in a multi-resolution fashion using Laplacian pyramid decomposition to account for the multi-channel properties of the human visual system. For this purpose, metrics are defined for contrast, image brightness and saturation. The performance of the proposed method is evaluated using visual assessment and quantitative measures for contrast, luminance and saturation. The results show the efficiency of the method in enhancing details without affecting the colour balance or introducing saturation artefacts and illustrate the usefulness of fusion techniques for image enhancement applications