The abilities to understand the social interaction behaviors between a vehicle and its surroundings while predicting its trajectory in an urban environment are critical for road safety in autonomous driving. Social interactions are hard to explain because of their uncertainty. In recent years, neural network-based methods have been widely used for trajectory prediction and have been shown to outperform hand-crafted methods. However, these methods suffer from their lack of interpretability. In order to overcome this limitation, we combine the interpretability of a discrete choice model with the high accuracy of a neural network-based model for the task of vehicle trajectory prediction in an interactive environment. We implement and evaluate our model using the INTERACTION dataset and demonstrate the effectiveness of our proposed architecture to explain its predictions without compromising the accuracy.

For an efficient integration of autonomous vehicles on roads, human-like reasoning and decision making in complex traffic situations are needed. One of the key factors to achieve this goal is the estimation of the future behavior of the vehicles present in the scene. In this work, we propose a new approach to predict the motion of vehicles surrounding a target vehicle in a highway environment. Our approach is based on an LSTM encoder-decoder that uses a social pooling mechanism to model the interactions between all the neighboring vehicles. The originality of our social pooling module is that it combines both local and non-local operations. The non-local multi-head attention mechanism captures the relative importance of each vehicle despite the inter-vehicle distances to the target vehicle, while the local blocks represent nearby interactions between vehicles. This paper compares the proposed approach with the state-of-the-art using two naturalistic driving datasets: Next Generation Simulation (NGSIM) and the new highD Dataset. The proposed method outperforms existing ones in terms of RMS values of prediction error, which shows the effectiveness of combining local and non-local operations in such a context.

In this paper, we explore the texture representation at high scale levels for the application of tree bark identification. We mainly propose an Improved Statistical Radial Binary Pattern (ISRBP) texture descriptor, by introducing a new representation of the scale-space to encode large macrostructures with a low-dimensional representation. The proposed descriptor has advantages of a compact and information-preserving description of large macrostructures, computational simplicity, no preprocessing stage, enhanced texture representativeness and discriminative power. We conducted comprehensive experiments on different bark datasets, and the results show the effectiveness of the new representation of scale-space. In addition, the combination of different statistical radial descriptors provides competitive to high identification rates than state-of-the-art LBP texture descriptors.

This paper presents a novel, yet compact texture descriptor for plant species identification based on bark texture images. Termed Statistical Macro Binary Pattern (SMBP), the descriptor is informative, rotation invariant, and it is designed to encode texture information from a large support area. The main novelty of this approach is the use of statistical description to represent the intensity distribution in the large support area, and an LBP-like encoding scheme to derive a statistical macro pattern by thresholding it against its adaptive statistical prototype. We propose to test three neighborhood sampling schemes according to the angular quantization at each level of the macrostructure. The comprehensive experiments on three challenging bark datasets (BarkTex, Trunk12, AFF) show that our descriptor achieves high and more consistent identification rates when compared with LBP-like texture descriptors.

This paper presents several improvements of [email protected], an image sharing and retrieval application for identifying plants [6]: (i) ported to most android platforms (ii) three times more data (iii) exploiting metadata as well as visual content in the identification process (iv) a new multi-plant-organ, multi-image and multi-feature merging strategy with separate indexes for each visual feature (v) integrating cross-languages functions. This paper also presents the new results achieved by our system in the ImageCLEF 2013 plant identification task and in real-world user trials.

In this paper, we propose an automatic approach for plant species identification, based on the visual information provided by the plant leaves. More precisely, we consider two sources of information: the leaf margin and the leaf salient points. We investigate two shape context based descriptors: the first one describes the leaf boundary while the second descriptor represents the spatial correlation between salient points of the leaf and its margin. We also study the performance of the fusion of these two descriptors on the Image-CLEF 2011 and 2012 leaf datasets. Experiments show the effectiveness and the efficiency of the proposed method.

There has recently been increasing interest in using advanced computer vision techniques for automatic plant identification. Most of the approaches proposed are based on an analysis of leaf characteristics. Nevertheless, two aspects have still not been well exploited: (1) domain-specific or botanical knowledge (2) the extraction of meaningful and relevant leaf parts. In this paper, we describe a new automated technique for leaf image retrieval that attempts to take these particularities into account. The proposed method is based on local representation of leaf parts. The part-based decomposition is defined and usually used by botanists. The global image query is a combination of part sub-images queries. Experiments carried out on real world leaf images, the Pl@ntLeaves scan images (3070 images totalling 70 species), show an increase in performance compared to global leaf representation.

Unravelling mysteries of the diversity of the plant community is a crucial issue both for the development of many botanical industries as well as for the conservation of ecosystem biodiversity. Traditionally, botanists have proposed detailed dichotomous key descriptions (called also characters or concepts) about the morphology of plants and particularly of leaves that allow them to construct relationships between different plants and between plants and their environment. However, extracting these concepts is complicated, painstaking and can only be carried out by experts. One way to accelerate and broaden the use of these concepts is to automatically extract them directly from images. In this paper, we focus on one of the most basic and important concepts: the leaf arrangement. According to this concept, leaves are divided into four categories: simple, pinnnately compound, palmately compound and compound trifoliate. To accomplish this task, we follow a hierarchical scheme, reducing ambiguity between categories from the most different shapes to the most similar ones. The choice of appropriate features is performed based on botanical observations and validated by a statistical study. The method was tested on real world leaf images (the Pl@ntLeaves scans). Experimental results show its robustness for a high number of leaf species (70 species) and even in the presence of some distortions (such as rotation and partial leaf overlapping).

In this paper we introduce a new multiscale shape-based approach for leaf image retrieval. The leaf is represented by local descriptors associated with margin sample points. Within this local description, we study four multiscale triangle representations: the well known triangle area representation (TAR), the triangle side lengths representation (TSL) and two new representations that we denote triangle oriented angles (TOA) and triangle side lengths and angle representation (TSLA). Unlike existing TAR approaches, where a global matching is performed, the similarity measure is based on a locality sensitive hashing of local descriptors. The proposed approach is invariant under translation, rotation and scale and robust under partial occlusion. Evaluations made on four public leaf datasets show that our shape-based approach achieves a high retrieval accuracy w.r.t. state-of-art methods.

This paper presents an Android application for plant identification. The system relies on the observation of leaf images. Unlike other mobile plant identification applications, the user may choose the leaf characters that will guide the identification process. For this purpose, two kinds of descriptors are proposed to the user: a shape descriptor based on a multiscale triangular representation of the leaf margin and a descriptor of the salient points of the leaf. The application achieves good identification accuracy and provides Android users a useful system for plant identification.

[email protected] is an image sharing and retrieval application for the identification of plants, available on iPhone and iPad devices. Contrary to previous content-based identification applications it can work with several parts of the plant including flowers, leaves, fruits and bark. It also allows integrating user's observations in the database thanks to a collaborative workflow involving the members of a social network specialized on plants. Data collected so far makes it one of the largest mobile plant identification tool.

Automatic plant identification is a relatively new research area in computer vision that has increasingly attracted high interest as a promising solution for the development of many botanical industries and for the success of biodiversity conservation. Most of the approaches proposed are based on the analysis of morphological properties of leaves. They have applied several well-known generic shape descriptors. Nevertheless, faced with the large amount of leaf species, botanical knowledge, especially about leaf parts (petiole, blade and their insertion point) is important to enhance their precision, hence, a crucial need to extract them from image. In this paper, we propose a fully automatic approach for petiole detection, based on the concept of local translational symmetry, which is applied to a some regions of the leaf. These regions are chosen w.r.t their size (small) taking into account the large diversity of leaf morphology (compound, oblong, orbicular). This method has been tested on two datasets and has provided more than 90% of correct detections.

The problem of automatic leaf identification is particularly challenging because, in addition to constraints derived from image processing such as geometric deformations (rotation, scale, translation) and illumination variations, it involves difficulties arising from foliar properties. These include two main aspects: the first is the enormous number and diversity of leaf species and the second, which is relevant to some special species, is the high inter-species and the low intra-species similarity. In this paper, we present a novel boundary-based approach that attempts to overcome the most of these constraints. This method has been compared to results obtained in the image CLEF 2011 plant identification task. The main advantage of this first benchmark edition is that different image retrieval techniques were tested and a crowd-sourced leaf dataset was used. Our method provides the best classification rate for scan and scan-like pictures. Besides its high accuracy, our method satisfies real-time requirements with a low computational cost.

Tracking epidemic disease is a very challenging issue nowadays. The success of such process could help medical administration to stop diseases quicker than usual. In this paper, we suggest a methodology based on wireless sensor networks deployed over volunteers who agree to carry a light wireless sensor network. Sensors over the body will monitor some health parameters (temperature, pressure,...) and will run some light classification algorithms to diagnosis first diseases on the volunteers and later on their neighbors. The classification methodologies used in this study are based on the SVM approach or on the Fuzzy C-Means one. Finally, the wireless sensor network will send aggregated data about the disease to some base stations which collect the results. The main contribution is to execute an on-line disease tracking program and to detect some information about how the disease is propagated.

This paper presents a novel method for leaf species identification combining local and shape-based features. Our approach extends the shape context model in two ways. First of all, two different sets of points are distinguished when computing the shape contexts: the voting set, i.e. the points used to describe the coarse arrangement of the shape and the computing set containing the points where the shape contexts are computed. This representation is enriched by introducing local features computed in the neighborhood of the computing points. Experiments show the effectiveness of our approach.

Content-based image retrieval has shown to be more and more useful for several application domains, from audiovisual media to security. As content-based retrieval became mature, different scientific applications were revealed client for such methods. More recently, botanical applications generated very large image collections and then became very demanding content-based visual similarity computation. In this paper, we describe low-level feature extraction for visual appearance comparison between genetically modified plants for gene expression studies.

The growing availability of multimedia data such as video on personal computers and home equipment creates a strong requirement for efficient tools to manipulate this type of data. Automatic summarization is one of these tools, which automatically creates a short version or subset of key-frames which contains as much information as possible as in the original video. Several approaches have been proposed to define and identify what is the most important content in a video. In this paper, we propose a new approach for the automatic creation of summaries for multi-episode videos, such as TV series. In this case, it is necessary to identify similarities and differences among videos (what’s common, what’s unique, how they differ) in order to find which elements best characterize a particular video with respect to the others. We describe our proposed method, provide the results of its application on a sample set of videos, and suggest a new criterion to evaluate the quality of the summaries that have been created.

This paper presents a comparison of some methodologies for the automatic construction of video summaries. The work is based on the simulated user principle to evaluate the quality of a video summary in a way which is automatic, yet related to user's perception. The method is studied for the case of multi-episode video, where we do not only describe what is important in a video, but rather what distinguishes this video from the others. Experimental results are presented to support the proposed ideas.