Dario Allegra

A Benchmark Dataset to Study the Representation of Food Images

It is well-known that people love food. However, an insane diet can cause problems in the general health of the people. Since health is strictly linked to the diet, advanced computer vision tools to recognize food images (e.g. acquired with mobile/wearable cameras), as well as their properties (e.g., calories), can help the diet monitoring by providing useful information to the experts (e.g., nutritionists) to assess the food intake of patients (e.g., to combat obesity). The food recognition is a challenging task since the food is intrinsically deformable and presents high variability in appearance. Image representation plays a fundamental role. To properly study the peculiarities of the image representation in the food application context, a benchmark dataset is needed. These facts motivate the work presented in this paper. In this work we introduce the UNICT-FD889 dataset. It is the first food image dataset composed by over \(800\) distinct plates of food which can be used as benchmark to design and compare representation models of food images. We exploit the UNICT-FD889 dataset for Near Duplicate Image Retrieval (NDIR) purposes by comparing three standard state-of-the-art image descriptors: Bag of Textons, PRICoLBP and SIFT. Results confirm that both textures and colors are fundamental properties in food representation. Moreover the experiments point out that the Bag of Textons representation obtained considering the color domain is more accurate than the other two approaches for NDIR.

Retrieval and classification of food images

Automatic food understanding from images is an interesting challenge with applications in different domains. In particular, food intake monitoring is becoming more and more important because of the key role that it plays in health and market economies. In this paper, we address the study of food image processing from the perspective of Computer Vision. As first contribution we present a survey of the studies in the context of food image processing from the early attempts to the current state-of-the-art methods. Since retrieval and classification engines able to work on food images are required to build automatic systems for diet monitoring (e.g., to be embedded in wearable cameras), we focus our attention on the aspect of the representation of the food images because it plays a fundamental role in the understanding engines. The food retrieval and classification is a challenging task since the food presents high variableness and an intrinsic deformability. To properly study the peculiarities of different image representations we propose the UNICT-FD1200 dataset. It was composed of 4754 food images of 1200 distinct dishes acquired during real meals. Each food plate is acquired multiple times and the overall dataset presents both geometric and photometric variabilities. The images of the dataset have been manually labeled considering 8 categories: Appetizer, Main Course, Second Course, Single Course, Side Dish, Dessert, Breakfast, Fruit. We have performed tests employing different representations of the state-of-the-art to assess the related performances on the UNICT-FD1200 dataset. Finally, we propose a new representation based on the perceptual concept of Anti-Textons which is able to encode spatial information between Textons outperforming other representations in the context of food retrieval and Classification.

On the Exploitation of One Class Classification to Distinguish Food Vs Non-Food Images

In the last years automatic food image understanding has become an important research challenge for the society. This is because of the serious impact that food intake has in human life. Food recognition engines, can help the monitoring of the patient diet and his food intake habits. Nevertheless, distinguish among different classes of food is not the first question for assisted dietary monitoring systems. Prior to ask what class of food is depicted in an image, a computer vision system should be able to distinguish between food vs non-food images. In this work we consider one-class classification method to distinguish food vs non-food images. The UNICT-FD889 dataset is used for training purpose, whereas other two datasets of food and non-food images has been downloaded from Flickr to test the method. Taking into account previous works, we used Bag-of-Words representation considering different feature spaces to build the codebook. To give possibility to the community to work on the considered problem, the datasets used in our experiments are made publicly available.

An Electronic Travel Aid to Assist Blind and Visually Impaired People to Avoid Obstacles

When devices and applications provide assistance to people they become part of assistive technology. If the assistance is given to impaired people, then it is possible to refer those technologies as adaptive technologies. The main aims of these systems are substitution of physical assistants and the improvement of typical tools already available for impaired people. In this paper some benefits and examples of adaptive technology applications will be discussed. Moreover we present an adaptive technology framework to avoid obstacles to be exploited by visually impaired and blind people. The proposed assistive technology has been designed to perform vision substitution; specifically it provides Electronic Travel Aid ETA capabilities through the processing of information acquired with a depth sensor such that the user can avoid obstacles during the environment exploration. In the proposed system we require to know just the height of the sensor with respect to the ground floor to calibrate the ETA system. Experiments are performed to asses the proposed system.

Tracking error in digitized analog video: automatic detection and correction

In the last half century the most used video storage devices have been the magnetic tapes, where the information are stored in analog format based on the electromagnetism principles. When the digital technique has become the most used, it was necessary to convert analog information in digital format in order to preserve these data. Unfortunately, analog videos may be affected by drops that produce some visual defect which could be acquired during the digitization process. Despite there are many hardware to perform the digitization, just few implement the automatic correction of these defects. In some cases, drop removal is possible through the analog device. However, when a damaged already-converted video is owned, a correction based on image processing technique is the unique way to enhance the videos. In this paper, the drop, also known as “Tracking Error” or “Mistracking,” is analyzed. We propose an algorithm to detect the drops’ visual artifacts in the converted videos, as well as a digital restoration method.

Integrated three-dimensional models for noninvasive monitoring and valorization of the Morgantina silver treasure (Sicily)

Abstract. The Morgantina silver treasure belonging to the Archaeological Museum of Aidone (Sicily) was involved in a three-dimensional (3-D) survey and diagnostics campaign for monitoring the collection over time in anticipation of their temporary transfer to the Metropolitan Museum of Art in New York for a period of 4 years. Using a multidisciplinary approach, a scientific and methodological protocol based on noninvasive techniques to achieve a complete and integrated knowledge of the precious items and their conservation state, as well as to increase their valorization, has been developed. All acquired data, i.e., 3-D models, ultraviolet fluorescence, x-ray images, and chemical information, will be made available, in an integrated way, within a web-oriented platform, which will present an in-progress tool to deepen existing archaeological knowledge and production technologies and to obtain referenced information of the conservation state before and after moving of the collection from its exposure site.

Virtual Anastylosis of Greek Sculpture as Museum Policy for Public Outreach and Cognitive Accessibility

This paper deals with a virtual anastylosis of a Greek Archaic statue from ancient Sicily and the development of a public outreach protocol for those with visual impairment or cognitive disabilities through the application of three-dimensional (3-D) printing and haptic technology. The case study consists of the marble head from Leontinoi in southeastern Sicily, acquired in the 18th century and later kept in the collection of the Museum of Castello Ursino in Catania, and a marble torso, retrieved in 1904 and since then displayed in the Archaeological Museum of Siracusa. Due to similar stylistic features, the two pieces can be dated to the end of the sixth century BC. Their association has been an open problem, largely debated by scholars, who have based their hypotheses on comparisons between pictures, but the reassembly of the two artifacts was never attempted. As a result the importance of such an artifact, which could be the only intact Archaic statue of a kouros ever found in Greek Sicily, has not fully been grasped by the public. Consequently, the curatorial dissemination of the knowledge related with such artifacts is purely based on photographic material. As a response to this scenario, the two objects have been 3-D scanned and virtually reassembled. The result has been shared digitally with the public via a web platform and, in order to include increased accessibility for the public with physical or cognitive disabilities, copies of the reassembled statue have been 3-D printed and an interactive test with the 3-D model has been carried out with a haptic device.

A Multimedia Database for Automatic Meal Assessment Systems

A healthy diet is crucial for maintaining overall health and for controlling food-related chronic diseases, like diabetes and obesity. Proper diet management however, relies on the rather challenging task of food intake assessment and monitoring. To facilitate this procedure, several systems have been recently proposed for automatic meal assessment on mobile devices using computer vision methods. The development and validation of these systems requires large amounts of data and although some public datasets already exist, they don’t cover the entire spectrum of inputs and/or uses. In this paper, we introduce a database, which contains RGB images of meals together with the corresponding depth maps, 3D models, segmentation and recognition maps, weights and volumes. We also present a number of experiments on the new database to provide baselines performances in the context of food segmentation, depth and volume estimation.

Low cost handheld 3D scanning for architectural elements acquisition

3D scanning has gone a long way since its first appearance in cultural heritage digitization and modeling. In the recent years some new low cost, fast, accurate emerging technologies are flooding the market. Envisioning the massive use of these cheap and easy to use devices in the next years, it is crucial to explore the possible fields of application and to test their effectiveness in terms of easiness of 3D data collection, processing, mesh resolution and metric accuracy against the size and features of the objects. In this study we focus the attention on one emerging technology, the Structure Sensor device, in order to verify a 3D pipeline acquisition on an architectural element and its details. The methodological approach is thought to define a pipeline of 3D acquisition exploiting low cost and open source technologies and foresees the assessment of this procedure in comparison with data obtained by a Time of Flight device.

Breast Shape Parametrization Through Planar Projections

In the last years, 3D scanning has replaced the low tech approach to acquire direct anthropometric measurements. These new methodologies provide a detailed digital model of the body and allow analysis of more complex information like volume, shape, curvature, and so on. The possibility to acquire the shape of soft tissues, such as the female human breast, has attracted the interest breast surgery specialists. The main aim of this work is to propose an innovative strategy to automatically analyze 3D breast shape in order to describe them within a quantitative well defined framework. In particular we propose a scanning procedure for a proper acquisition of breast surfaces by using the handheld scanner Structure Sensor, as well as a framework to process 3D digital data to extract the shape information. The proposed method consists in two main parts: firstly, the acquired digital 3D surfaces are projected in a 2D space and a set of 17 geometrical landmarks are extracted; then by exploiting Thin Plate Splines and Principal Components Analysis the original data are summarised and the breast shape is described by a small set of numerical parameters.