A computer vision system for appearance-based descriptive sensory evaluation of meals

This paper presents a complete machine vision system for automatic descriptive sensory evaluation of meals. A human sensory panel first developed a set of 72 sensory attributes describing the appearance of a prototypical meal, and then evaluated the intensities of those attributes on a data set of 58 images of example meals. This data was then used both to train and validate the performance of the artificial system. This system covers all stages of image analysis from pre-processing to pattern recognition, including novel techniques for enhancing the segmentation of meal components and extracting image features that mimic the attributes developed by the panel. Artificial neural networks were used to learn the mapping from image features to attribute intensity values. The results showed that the new system was extremely good in learning and reproducing the opinion of the human sensory experts, achieving almost the same performance as the panel members themselves

Descriptive Text Writing Skills Through Picture Media in Elementary School Students

This study aimed to describe the skills of writing descriptive text through image media in the form of identification structures or general statements and section description structures in class VII students at SMP Negeri 2 Genteng. The research data is in the form of essays/the results of learning the skills of writing descriptive text through the media of images in class VII B students at SMP Negeri 2 Genteng. Data collection techniques used documentation techniques which were carried out through five stages, namely (1) research questions, (2) determining category definitions and levels of abstraction, (3) category formulation, (4) category revision, and (5) final work. The research instrument is the researcher himself as the main instrument and is assisted by supporting instruments in the form of assessment instrument tables. The data in the study were analyzed through four stages, namely: (1) reading and studying, (2) identifying, (3) presenting the results of the categorization, and (4) concluding the results of the analysis found. The technique for testing the validity of the data in this study uses the observation persistence technique. The results of the data analysis show that there are assessment indicators in the form of (1) identification structures or general statements which include object recognition. (2) the structure of the description of the part, which includes words that refer to the names of objects, material or action verbs, and adjectives. Based on the data results, this study concludes that students are categorized as good because students can write descriptive text through media images based on assessment indicators

Aggregated Deep Local Features for Remote Sensing Image Retrieval

Remote Sensing Image Retrieval remains a challenging topic due to the special nature of Remote Sensing Imagery. Such images contain various different semantic objects, which clearly complicates the retrieval task. In this paper, we present an image retrieval pipeline that uses attentive, local convolutional features and aggregates them using the Vector of Locally Aggregated Descriptors (VLAD) to produce a global descriptor. We study various system parameters such as the multiplicative and additive attention mechanisms and descriptor dimensionality. We propose a query expansion method that requires no external inputs. Experiments demonstrate that even without training, the local convolutional features and global representation outperform other systems. After system tuning, we can achieve state-of-the-art or competitive results. Furthermore, we observe that our query expansion method increases overall system performance by about 3%, using only the top-three retrieved images. Finally, we show how dimensionality reduction produces compact descriptors with increased retrieval performance and fast retrieval computation times, e.g. 50% faster than the current systems.Comment: Published in Remote Sensing. The first two authors have equal contributio

The Descriptive Challenges of Fiber Art

published or submitted for publicatio

Assessing the Effectiveness of Automated Emotion Recognition in Adults and Children for Clinical Investigation

Recent success stories in automated object or face recognition, partly fuelled by deep learning artificial neural network (ANN) architectures, has led to the advancement of biometric research platforms and, to some extent, the resurrection of Artificial Intelligence (AI). In line with this general trend, inter-disciplinary approaches have taken place to automate the recognition of emotions in adults or children for the benefit of various applications such as identification of children emotions prior to a clinical investigation. Within this context, it turns out that automating emotion recognition is far from being straight forward with several challenges arising for both science(e.g., methodology underpinned by psychology) and technology (e.g., iMotions biometric research platform). In this paper, we present a methodology, experiment and interesting findings, which raise the following research questions for the recognition of emotions and attention in humans: a) adequacy of well-established techniques such as the International Affective Picture System (IAPS), b) adequacy of state-of-the-art biometric research platforms, c) the extent to which emotional responses may be different among children or adults. Our findings and first attempts to answer some of these research questions, are all based on a mixed sample of adults and children, who took part in the experiment resulting into a statistical analysis of numerous variables. These are related with, both automatically and interactively, captured responses of participants to a sample of IAPS pictures