Agricultural product recognition system using taxonomist's knowledge as semantic attributes

Support Vector Machine (SVM) was used to classify type of produce commonly sold in supermarkets. We applied a sequence of image processing algorithms such as conversion of color space, thresholding and morphological operation to obtain the region of interest from the images. Global and local features were extracted from the images and used as input for the classifiers. The color and texture features extracted in this system were L*a*b* values and texton approach respectively. Since attribute learning has emerged as a promising paradigm for assisting in object recognition, we proposed to integrate it into our system. This could tackle problem occurred when less training data are available, i.e. less than 20 samples per class. The performances of the proposed classifier and conventional SVM were also compared. The experiments showed that the classification accuracy of the proposed classifier is higher than conventional SVM by 7% when only 4 samples per class were trained

Analysis of produce recognition system with taxonomist's knowledge using computer vision and different classifiers

Supermarkets nowadays are equipped with barcode scanners to speed up the checkout process. Nevertheless, most of the agricultural products cannot be pre-packaged and thus must be weighted. The development of produce recognition system based on computer vision could help the cashiers in the supermarkets with the pricing of these weighted products. This work proposes a hybrid approach of object classification and attribute classification for the produce recognition system which involves the cooperation and integration of statistical approaches and semantic models. The integration of attribute learning into the produce recognition system was proposed due to the fact that attribute learning has emerged as a promising paradigm for bridging the semantic gap and assisting in object recognition in many fields of study. This could tackle problems occurred when less training data are available, i.e. less than 10 samples per class. The experiments show that the correct classification rate of the hybrid approach were 60.55, 75.37 and 86.42% with 2, 4 and 8 training examples, respectively, which were higher than other individual classifiers. A well-balanced specificity, sensitivity and F1 score were achieved by the hybrid approach for each produce type

High imperceptibility medical image watermarking scheme based on slantlet transform by using dynamic visibility threshold

Digital watermarking is being used for increasing the level of effectiveness and security of the documents transfer over the internet, especially in the biomedical field. This study examined and explored the new working domain for medical image watermarking by introducing a Dynamic Visibility Threshold (DVT) as a crossover from pure mathematics over the traditional fixed visibility threshold. Initially, for providing higher robustness against the watermarking attacks, the watermarking process is done on Slantlet Transform, an improved version of Discrete Wavelet Transform. By investigating the root of the medical image properties, we introduce the descriptive statistics namely Quantile Theory to deliver a dynamic value from the biomedical itself for the embedding process. Later, we had identified that the Third Quartile is the most optimum value for the DVT in terms of higher imperceptibility. This paper uses a standard dataset from BRAINIX, a medical database provided by OsiriX, Pixmeo SARL, and the effectiveness of the proposed method is analysed using MATLAB R2013a. The most obvious finding to emerge from this study is the convergence of descriptive statistics from pure mathematics and medical image watermarking from computer science, therefore shine new light on dynamic visibility threshold through mathematical theories. With the presence of descriptive statistics, quartile theory is being used to increase the imperceptibility of the watermarking in biomedical images

Intelligent Manufacturing Planning System Using Dispatch Rules: A Case Study in Roofing Manufacturing Industry

This paper aims to investigate the optimal sorting of orders reflecting on the material changing lead time over the machines in the roofing manufacturing industry. Specifically, a number of jobs were sorted together based on the material used and then consolidated for subsequent processes, i.e., assigned to the corresponding machines. To achieve the optimal sorting for the received orders, a combinatorial dispatch rule was proposed, which were Earliest Due Date (EDD), First In First Out (FIFO), and Shortest Processing Time (SPT). The sequence of orders organized by the scheduling algorithm was able to minimize the changing material lead time and also maximize the number of orders to be scheduled in the production. Consequently, on-time delivery could be achieved. Tests based on real data have been set up to evaluate the performance of the proposed algorithm in sorting the received orders. As a result, the proposed algorithm has successfully reduced the material changing lead time by 47.3% and 40% in the first and second tests, respectively

University Examination Timetabling Using a Hybrid Black Hole Algorithm

University timetabling construction is a complicated task that is encountered by universities in the world. In this study, a hybrid approach has been developed to produce timetable solution for the university examination timetabling problem. Black Hole Algorithm (BHA), a population-based approach that mimics the black hole phenomenon has been introduced in the literature recently and successfully applied in addressing various optimization problems. Although its effectiveness has been proven, there still exists inefficiency regarding the exploitation ability where BHA is poor in fine tuning search region in reaching for good quality of solution. Hence, a hybrid framework for university examination timetabling problem that is based on BHA and Hill Climbing local search is proposed (hybrid BHA). The aim of this hybridization is to improve the exploitation ability of BHA in fine tuning the promising search regions and convergence speed of the search process. A real-world university examination benchmark dataset has been used to evaluate the performance of hybrid BHA. The computational results demonstrate that hybrid BHA capable of generating competitive results and recording best results for three instances, compared to the reference approaches and current best-known recorded in the literature. Other than that, findings from the Friedman tests show that the hybrid BHA ranked second and third in comparison with hybrid and meta-heuristic approaches (total of 27 approaches) reported in the literature, respectively