Hand gesture recognition serves as a key for overcoming many difficulties and providing convenience for human life. The ability of machines to understand human activities and their meaning can be utilized in a vast array of applications. One specific field of interest is sign language recognition. This paper provides a thorough review of state-of-the-art techniques used in recent hand gesture and sign language recognition research. The techniques reviewed are suitably categorized into different stages: data acquisition, pre-processing, segmentation, feature extraction and classification, where the various algorithms at each stage are elaborated and their merits compared. Further, we also discuss the challenges and limitations faced by gesture recognition research in general, as well as those exclusive to sign language recognition. Overall, it is hoped that the study may provide readers with a comprehensive introduction into the field of automated gesture and sign language recognition, and further facilitate future research efforts in this area.

A review of hand gesture and sign language recognition techniques

Hand gestures are a form of nonverbal communication that can be used in several fields such as communication between deaf-mute people, robot control, human–computer interaction (HCI), home automation and medical applications. Research papers based on hand gestures have adopted many different techniques, including those based on instrumented sensor technology and computer vision. In other words, the hand sign can be classified under many headings, such as posture and gesture, as well as dynamic and static, or a hybrid of the two. This paper focuses on a review of the literature on hand gesture techniques and introduces their merits and limitations under different circumstances. In addition, it tabulates the performance of these methods, focusing on computer vision techniques that deal with the similarity and difference points, technique of hand segmentation used, classification algorithms and drawbacks, number and types of gestures, dataset used, detection range (distance) and type of camera used. This paper is a thorough general overview of hand gesture methods with a brief discussion of some possible applications.

https://www.mdpi.com/2313-433X/6/8/73/pdf

Hand Gesture Recognition Based on Computer Vision: A Review of Techniques

Segmentation of images by color features: a survey

Natural hazards have the potential to cause catastrophic damage and significant socioeconomic loss. The actual damage and loss observed in the recent decades has shown an increasing trend. As a result, disaster managers need to take a growing responsibility to proactively protect their communities by developing efficient management strategies. A number of research studies apply artificial intelligence (AI) techniques to process disaster-related data for supporting informed disaster management. This study provides an overview of current applications of AI in disaster management during its four phases: mitigation, preparedness, response, and recovery. It presents example applications of different AI techniques and their benefits for supporting disaster management at different phases, as well as some practical AI-based decision support tools. We find that the majority of AI applications focus on the disaster response phase. This study also identifies challenges to inspire the professional community to advance AI techniques for addressing them in future research.

Applications of artificial intelligence for disaster management

Human Skin detection deals with the recognition of skin-colored pixels and regions in a given image. Skin color is often used in human skin detection because it is invariant to orientation and size and is fast to process. A new human skin detection algorithm is proposed in this paper. The three main parameters for recognizing a skin pixel are RGB (Red, Green, Blue), HSV (Hue, Saturation, Value) and YCbCr (Luminance, Chrominance) color models. The objective of proposed algorithm is to improve the recognition of skin pixels in given images. The algorithm not only considers individual ranges of the three color parameters but also takes into ac- count combinational ranges which provide greater accuracy in recognizing the skin area in a given image.

/pdf/human-skin-detection-using-rgb-hsv-and-ycbcr-color-models-26l3bxic55.pdf

Human Skin Detection Using RGB, HSV and YCbCr Color Models

Comparative Study of Skin Color Detection and Segmentation in HSV and YCbCr Color Space

In this paper, a new method for the extraction of the necessary object or region from the satellite image is proposed. This color based method is based on HSV color space and histogram threshold. Usually the multispectral images received from satellite are in RGB color space. But human eye is more sensitive to intensity than color information (hue or saturation). In the proposed approach, the satellite image in RGB color space is transformed into HSV color space and then the transformed satellite image is split into three different components (channels or images) based on intensity and color. This HSV color space is designed to approximate the human vision. In the next step, the histogram for all three components (hue, saturation and value) is computed and plotted. Then the threshold value is separately applied to all three components. Finally the morphological operations like masking, filtering, smoothening is performed for the extraction of the desired region. The effectiveness of the proposed approach has been demonstrated by number of experiments. Experimental result demonstrates that the proposed method is more efficient in the extraction and segmentation of a desired object or region in the satellite images.

HSV color space based segmentation of region of interest in satellite images

Possiblistic-Fuzzy C-Means Clustering Approach for the Segmentation of Satellite Images in HSL Color Space☆

Segmentation is one of the most important processes in the satellite image processing to retrieve most useful information from the satellite images. This paper proposed an effective fuzzy based method of segmentation of satellite images in YCbCr color space. The YCbCr Color space represents color as intensity and exploits the characteristics of human eye. Our eye is more sensitive to intensity than hue. The intensity component can be stored with greater accuracy as the amount of information to be minimized. The JPEG file format mostly uses this color space to discard the unwanted or unimportant information. In the proposed approach, the satellite image in RGB color space is transform into YCbCr color space and then the transformed satellite image is split into three different components (channels or images) based on luminance and chrominance. Subsequently Fuzzy based segmentation is applied separately for all three components for efficient segmentation. Finally the threshold is applied to extract the foreground (object) from the background. The experimental result reveals that the proposed fuzzy based segmentation method is efficient and accurate for extracting the necessary information from the satellite images.

Satellite Image Segmentation based on YCbCr Color Space

Forest, complex ecosystem, is a shelter for all living things such as plants, animals, birds and various resources such as lakes, rivers, minerals. Forests cover approximately 30% of land on the earth. As compared to forest expansion by natural or planting, the deforestation rate is always higher. Forest fire is the most notorious danger which entirely ruined the environment of the forest. These giant size fires can spread out and change the direction rapidly from its source. It is very difficult to monitor and control the fires in remote and vast areas like forest. In this research work modified fuzzy c-means clustering approach had proposed for the identification and the extraction of the region of forest fires. The experiment is conducted on both RGB and CIELab color space. The outcome of the modified fuzzy c-means clustering method had compared with K-means clustering method based on the evaluation of the image quality parameters. Experimental results had demonstrated the efficiency for the proposed approach for the detection and segmentation of forest fires in high resolution satellite images.

B. S. Sathish

Papers

Comparative Study of Skin Color Detection and Segmentation in HSV and YCbCr Color Space

HSV color space based segmentation of region of interest in satellite images

Possiblistic-Fuzzy C-Means Clustering Approach for the Segmentation of Satellite Images in HSL Color Space☆

Satellite Image Segmentation based on YCbCr Color Space

A comparative approach of identification and segmentation of forest fire region in high resolution satellite images