http://profdoc.um.ac.ir/articles/a/1077079.pdf

Robotic hand: A review on linkage-driven finger mechanisms of prosthetic hands and evaluation of the performance criteria

In this paper, an adapted unsupervised segmentation approach is proposed to fully automate the segmentation of white blood cells and their nuclei. Segmentation and counting of white blood cells from microscope images are challenging tasks, especially the segmentation of white blood cell nuclei from the cell wall and cytoplasm because of the need to consider intra-class variations arising from non-uniform illumination, stage of maturity, colour distribution, scale, and overlapped cells with other components of the blood. We propose the use of the JSEG algorithm based on colour-texture distribution, and adapted region growing using the Fuzzy C Mean to segment and count WBCs and their nuclei. First, colours in the image are quantized to represent differentiated regions in the image. Image pixel colours are then replaced by their corresponding colour class labels, thus forming a class-map of the image. A criterion for “good” segmentation using this spatial class-map is applied to local image windows resulting in J-images, which can be segmented using adapted region growing based on the Fuzzy C Mean algorithm. The Fuzzy C Mean is also employed for counting each white blood cell in images. Performance of the proposed method is evaluated on a combined dataset of 10 types of white blood cell with 200 digital images collected from 3 datasets. It achieves an average segmentation accuracy using four indices for WBC segmentation: jaccard distance, rand index, boundary detection error and F-value indices, 0.002, 0.93, 10.11, 0.93, respectively, while for WBC nuclei segmentation, it achieves indices values, 0.015, 0.88, 14.11, 0.90, respectively. The segmentation accuracy of the proposed method is also compared and benchmarked with the other existing techniques for segmentation of white blood cells over the same datasets and the results show that the proposed method is superior to other approaches.

/pdf/using-adapted-jseg-algorithm-with-fuzzy-c-mean-for-33qbdy7gbw.pdf

Using Adapted JSEG Algorithm with Fuzzy C Mean for Segmentation and Counting of White Blood Cell and Nucleus Images

Human hand is the best sample for humanoid robotic hand and a nature grasping is the final target that most robotic hands are pursuing. Many prior researches had been done in virtual and real for simulation the human grasping. Unfortunately, there is no perfect solution to duplicate the nature grasping of human. The main difficulty comes from three points. 1. How to 3D modelling and fabricate the real hand. 2. How actuated the robotic hand as real hand. 3. How to grasp objects in different shapes like human hand. To deal with these three problems and further to provide a partial solution for duplicate human grasping, this paper introduces our method to solve these problems from robotic hand design, fabrication, actuation and grasping plan. Our modelling progress takes only around 12 minutes that include 10 minutes of 3D scanning of a real human hand and two minutes for changing the scanned model to an articulated model by running our algorithm. Our grasping plan is based on the sampled trajectory and easy to implement for grasping different objects. Followed these steps, a seven DOF robotic hand is created and tested in the experiments.

/pdf/nature-grasping-by-a-cable-driven-under-actuated-3ej55f98bd.pdf

Nature grasping by a cable-driven under-actuated anthropomorphic robotic hand

The original JSEG algorithm has proved to be very useful and robust in variety of image segmentation case studies.However, when it is applied into the underwater coral reef images, the original JSEG algorithm produces over-segementation problem, thus making this algorithm futile in such a situation. In this paper, an approach to reduce the over-segmentation problem occurred in the underwater coral reef image segmentation is presented. The approach works by replacing the color histogram computation in region merge stage of the original JSEG algorithm with the new computation of color and texture features in the similarity measurement. Based on the perceptual observation results of the test images, the proposed modified JSEG algorithm could automatically segment the regions better than the original JSEG algorithm.

/pdf/modified-jseg-algorithm-for-reducing-over-segmentation-3ckl7djveh.pdf

Modified JSEG algorithm for reducing over-segmentation problems in underwater coral reef images

The Use of Immersive Technologies in Industry 4.0: A State-of-Art Review

Image tactile interaction is a new interaction between human and digital images, allowing users to know digital images by touch. In order to improve the authenticity of image tactile perception, this paper proposes a tactile perception model based on image region features. Aiming at the phenomenon of over-partitioning and computational complexity of JSEG algorithm, we propose an improved JSEG algorithm, which can effectively reduce the computational complexity and divide the image areas which are more in line with the subjective visual judgment, and can be used for region-based image tactile generation. The experimental results show that the proposed algorithm can correctly distinguish the image area and improve the accuracy of image tactile perception.

Image Tactile Perception with an Improved JSEG Algorithm

Manipulating virtual objects using our real hands is a great challenge for the virtual reality community. We present a master-slave hand system to naturally manipulate virtual objects with a user's hand. The master-slave hand system can obtain the position, orientation and finger joint angle of the user's hand, which is used to drive a dexterous virtual hand to interact with virtual environments. The dexterous virtual hand we modeled has analogous motion function of the real hand. Simplified virtual hand manipulation intentions we defined help to the dexterous virtual hand manipulating virtual objects conveniently. A virtual assembly system prototype validates that this master-slave hand system attains intuitive and flexible hands-on interaction with virtual environments.

A Master-Slave Hand System for Virtual Reality Interaction

Finger workspace is one of the most important features to evaluate the kinematic performances of the multi-finger hands. It is also benefit for grasp planning, mechanical design, and motion control. Although many multi-finger hands have been developed over the last few decades, how to generate and visualize their workspaces is still a challengeable problem. We propose a workspace generation and visualization approach to explain the workspace characteristics of a multi-finger hand (ZSTU hand), which can help to solve the problem of choosing a grasp with the gripper constrains. Compared with previous researches, this approach can model more integrated fingertip workspaces with vivid forms and contours, which is valuable for researchers to accurately and intuitionally understand the fingertip motion ranges and the analysis of the workspace volume. These performances can help researchers to optimize the mechanical structures of the multi-finger hand, and improve the kinematic performances of the multi-finger hand.

A Workspace Modeling Approach for Multi-finger Hands

The intersection workspace of multi-finger hands means the overlapping workspace in 3D Euclidean space where several fingertips can reach together, and is very valuable for grasp space planning, motion control and mechanical design for multi-finger hands. But, the intersection workspaces usually have sophisticated forms, which are really hard for robotic experts to image them. We present a general approach to visualize and analyze the intersection workspace of multi-finger hands. We firstly deduce the displacement equation of the fingertip, then generate 3D workspace of the fingertip and lastly obtain the intersection workspace of the multi-finger hands. This approach can visualize intersection workspaces with integrated forms and contours, and analyze the volume of the intersection workspaces. The vivid form and volume analysis of intersection workspaces are useful for robotic experts to accurately and intuitionally understand the fingertip motion ranges, plan the motion routes of fingers and avoid the motion collision among fingers.

Intersection workspace visualization of multi-finger hands

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Image Tactile Perception with an Improved JSEG Algorithm

A Master-Slave Hand System for Virtual Reality Interaction

A Workspace Modeling Approach for Multi-finger Hands

Intersection workspace visualization of multi-finger hands