Automated Curb Recognition and Negotiation for Robotic Wheelchairs

Common electric powered wheelchairs cannot safely negotiate architectural barriers (i.e., curbs) which could injure the user and damage the wheelchair. Robotic wheelchairs have been developed to address this issue; however, proper alignment performed by the user is needed prior to negotiating curbs. Users with physical and/or sensory impairments may find it challenging to negotiate such barriers. Hence, a Curb Recognition and Negotiation (CRN) system was developed to increase user’s speed and safety when negotiating a curb. This article describes the CRN system which combines an existing curb negotiation application of a mobility enhancement robot (MEBot) and a plane extraction algorithm called Polylidar3D to recognize curb characteristics and automatically approach and negotiate curbs. The accuracy and reliability of the CRN system were evaluated to detect an engineered curb with known height and 15 starting positions in controlled conditions. The CRN system successfully recognized curbs at 14 out of 15 starting positions and correctly determined the height and distance for the MEBot to travel towards the curb. While the MEBot curb alignment was 1.5 ± 4.4°, the curb ascending was executed safely. The findings provide support for the implementation of a robotic wheelchair to increase speed and reduce human error when negotiating curbs and improve accessibility.

Security Analysis and Improvement of an Image Encryption Cryptosystem Based on Bit Plane Extraction and Multi Chaos

This paper analyzes the security of image encryption systems based on bit plane extraction and multi chaos. It includes a bit-level permutation for high, 4-bit planes and bit-wise XOR diffusion, and finds that the key streams in the permutation and diffusion phases are independent of the plaintext image. Therefore, the equivalent diffusion key and the equivalent permutation key can be recovered by the chosen-plaintext attack method, in which only two special plaintext images and their corresponding cipher images are used. The effectiveness and feasibility of the proposed attack algorithm is verified by a MATLAB 2015b simulation. In the experiment, all the key streams in the original algorithm are cracked through two special plaintext images and their corresponding ciphertext images. In addition, an improved algorithm is proposed. In the improved algorithm, the generation of a random sequence is related to ciphertext, which makes the encryption algorithm have the encryption effect of a “one time pad”. The encryption effect of the improved algorithm is better than that of the original encryption algorithm in the aspects of information entropy, ciphertext correlation analysis and ciphertext sensitivity analysis.

A Plane Extraction Approach in Inverse Depth Images Based on Region-Growing

Planar surfaces are prevalent components of man-made indoor scenes, and plane extraction plays a vital role in practical applications of computer vision and robotics, such as scene understanding, and mobile manipulation. Nowadays, most plane extraction methods are based on reconstruction of the scene. In this paper, plane representation is formulated in inverse-depth images. Based on this representation, we explored the potential to extract planes in images directly. A fast plane extraction approach, which employs the region growing algorithm in inverse-depth images, is presented. This approach consists of two main components: seeding, and region growing. In the seeding component, seeds are carefully selected locally in grid cells to improve exploration efficiency. After seeding, each seed begins to grow into a continuous plane in succession. Both greedy policy and a normal coherence check are employed to find boundaries accurately. During growth, neighbor coplanar planes are checked and merged to overcome the over-segmentation problem. Through experiments on public datasets and generated saw-tooth images, the proposed approach achieves 80.2% CDR (Correct Detection Rate) on the ABW SegComp Dataset, which has proven that it has comparable performance with the state-of-the-art. The proposed approach runs at 5 Hz on typical 680 × 480 images, which has shown its potential in real-time practical applications in computer vision and robotics with further improvement.

Leaf Disease Detection using Labview Imaq Vision

The intension of our project is to design a system which can identify the good leaves from the diseased ones. Image processing is a powerful tool capable of many applications. Image processing combined with Machine Vision can simulate and execute real time projects. In this project we have used LabVIEW along with IMAQ Vision to acquire real time images and process them. LabVIEW IMAQ Vision is potentially useful for agricultural products since it combines the merits of both LabVIEW and IMAQ Vision, which have graphical programming environment and rich image processing functions. The project aims to provide a brief introduction into the IMAQ vision components like Image Acquisition, Calibration, Defect detection. Major leaf diseases’ symptoms include spots or discolouration of leaves. The presence or absence of macro and micro nutrients, bug infestation and other diseases can be identified through leaves. In this project we have obtained the images through LabVIEW IMAQ vision pallet. Further on two procedures were followed – one based on colour of the leaves and other is based on spots and patterns present on the leaves. For the discolouration we first split the image into its constituent planes- RGB and CMYK, here we used Green, Cyan and Yellow planes. Then on we decided a threshold based on sample data using Linear Regression based prediction model of Machine Learning to classify the data into three states – safe, risk and high risk.The second method was detecting spots. First, we split the images into its constituent planes to convert the RGB image to Greyscale and increase the contrast using the Colour Plane Extraction tool then use the Look up table tool to further enhance the contrast. Then on locate the bright objects and then using dilation from the Morphology tool box we increase the size of the spots to increase detection rate. Using Advanced Morphology tool box we removed the boundary objects to isolate the spots. Then using the shape detection or circle detection algorithm we can detect the spots. Several samples were obtained and are successfully classified. Finally, current limitations and likely future development trends are discussed. Combining LabVIEW along with different programming algorithms can help in raising the accuracy of the system.