17 research outputs found
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No full textIn this paper we describe a statistical video representation and modeling scheme. Video representation schemes are needed to segment a video stream into meaningful video-objects, useful for later indexing and retrieval applications. In the proposed methodology, unsupervised clustering via Gaussian mixture modeling extracts coherent space-time regions in feature space, and corresponding coherent segments (video-regions) in the video content. A key feature of the system is the analysis of video input as a single entity as opposed to a sequence of separate frames. Space and time are treated uniformly. The probabilistic space-time video representation scheme is extended to a piecewise GMM framework in which a succession of GMMs are extracted for the video sequence, instead of a single global model for the entire sequence. The piecewise GMM framework allows for the analysis of extended video sequences and the description of nonlinear, nonconvex motion patterns. The extracted space-time regions allow for the detection and recognition of video events. Results of segmenting video content into static vs. dynamic video regions and video content editing are presented
Utilization of <i>Polygonum cognatum</i> Meissn, a Gastronomic Product of the Turkish Culinary Culture, in the Production of Gluten-free Biscuits
No full text<p>Polygonum cognatum Meissn. (Polygonaceae), locally known as "solucan otu" or "madimak" in Turkey, is a type of edible grass with functional properties that naturally grows in various regions of the country, particularly in Central Anatolia. Celiac disease is a prevalent digestive system disorder that impairs the absorption of food. Incorporating functional ingredients into gluten-free formulations can increase their value. To be a preferred alternative to commonly consumed products, a new product must be sensory-appealing. This study focuses on developing a healthy, functional, and enjoyable gluten-free cookie. Various proportions of P. cognatum flour (10%, 20%, 30%, and 40%) were utilized in the development of the product. The physical, textural, chemical, and sensory characteristics of the biscuit samples were compared to those of samples made with wheat flour (Control 1) and a gluten-free flour mixture (Control 2). The addition of P. cognatum flour proportionally increased the moisture content, ash value, dietary fiber, antioxidant, phenolic matter, and protein content of the biscuits without changing the fat content, according to experimental findings. Samples with 40% P. cognatum addition exhibited higher levels of moisture, ash, protein, dietary fiber, antioxidant, and phenolic matter compared with the other samples. However, the results of the sensory analysis revealed that utilizing more than 30% of P. cognatum flour significantly decreased consumer preference by negatively affecting sensory attributes such as texture, consistency, mouthfeel, taste, overall acceptability, color, and fracture. Hence, it was concluded that this type of flour should not exceed 30% when producing functional gluten-free biscuits. From this study, we advocate for the recognition of P. cognatum as a local and gastronomic product, which has the potential to be utilized in wider areas.</p>
Optimization of Gluten-Free Cookie Flour Formulation by Using Response Surface Methodology
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Determination of the severity level of yellow rust disease in wheat by using convolutional neural networks
No full textYellow rust disease caused by Puccinia striiformis f. sp. tritici, a pathogen in wheat, results in significant losses in wheat production worldwide due to its high destructive property. On the other side, yellow rust can be taken under control by growing resistant cultivars, by the application of fungicides, and by the use of appropriate cultural practices. Thus, it is crucial to detect the disease at an early stage. The current study offers to use computerized models in determining the infection type of yellow rust disease in wheat. Herein, a deep convolutional neural networks-based model, named Yellow-Rust-Xception, was proposed. The model inputs the wheat leaf image and classifies it as no disease, resistant, moderately resistant, moderately susceptible, or susceptible according to the rust severity, i.e. percentage. The convolutional neural networks, a state-of-art approach, have layered structures those inspired by the human brain and able to learn discriminative features from data automatically; thus networks performance match and even surpass humans in task-specific applications, a newly developed dataset containing yellow rust-infected wheat leaf images, was used to train, validate, and test Yellow-Rust-Xception, in result, the test accuracy was 91%. Thus, Yellow-Rust-Xception can be used in determining wheat yellow rust and its severity level.Scientific and Technological Research Council of Turkey (TuBTAK) [120O960]This study was supported with project 120O960 by The Scientific and Technological Research Council of Turkey (TuBTAK). In addition, we would like to thank the Republic of Turkey Ministry of Agriculture and Forestry Directorate of Field Crops Central Research Institute which allowed us to use its resources, Mehmet AYDOGDU who is a permanent worker there, smail KARAKAS who is we consulted for photoshoots and used his digital camera and Yozgat Bozok University Boazlyan Vocational High School Manager Asst. Prof. Mustafa KOCAKAYA supported us
Keyword spotting for cursive document retrieval
Get PDFWe present one of the first attempts towards automatic retrieval of documents, in the noisy environment of unconstrained, multiple author handwritten forms. The documents were written in cursive script for which conventional OCR and text retrieval engines are not adequate. We focus on a visual word spotting indexing scheme for scanned documents housed in the Archives of the Indies in Seville, Spain. The framework presented utilizes pattern recognition, learning and information fusion methods, and is motivated from human word-spotting studies. The proposed system is described and initial results are presented
Effects of Chickpea-Based Leavening Extract on Physical, Textural and Sensory Properties of White Wheat Bread
No full textThis study aimed to investigate the effects of chickpea-based leavening extract (CLE) on certain white wheat bread characteristics. CLE increased the loaf volume, height and redness while it reduced the moisture, lightness and yellowness of the bread. Although crumb hardness of CLE bread was observed to be higher than commercial baker's yeast (CBY; Saccharomyces cerevisiae) bread on the first day, this value did not show any significant difference during two days storage. Adhesiveness and chewiness of CLE bread were affected significantly, whereas springiness was not. Loaf volume, symmetry, crust colour, crust structure, texture, mouthfeel, odour, general acceptability and purchasing intent of CLE bread was scored higher as a result of sensorial analyses. We concluded that CLE could be used in breadmaking as an alternative to CBY for consumers who want a different taste and flavour. The use of CLE can be made widespread by performing and standardising the commercial production
