Daniel Ochoa

Leaf segmentation and tracking using probabilistic parametric active contours

Active contours or snakes are widely used for segmentation and tracking. These techniques require the minimization of an energy function, which is generally a linear combination of a data fit term and a regularization term. This energy function can be adjusted to exploit the intrinsic object and image features. This can be done by changing the weighting parameters of the data fit and regularization term. There is, however, no rule to set these parameters optimally for a given application. This results in trial and error parameter estimation. In this paper, we propose a new active contour framework defined using probability theory. With this new technique there is no need for ad hoc parameter setting, since it uses probability distributions, which can be learned from a given training dataset.

Edge Linking Based Method to Detect and Separate Individual C. Elegans Worms in Culture

Nowadays an increasing research interest in the field of biotechnology has been drawn to achieve reliable information from model organisms. C. Elegans nematode worm is one of the major animals. Machine vision analysis of this animal needs to solve many important problems, e.g. detection of each individual in population images, movement patterns of isolated and overlapped worms and so on. In this paper, we describe our recently proposed method with an analysis to show the impact of two major parameters on the method efficiency. Based on our analysis on 255 isolated and overlapped worms, we find that our method prepares the best correct detection (TAR=83%) for thetasTh and NTh between 15 and 20 pixels.

Unveiling E-Bike Potential for Commuting Trips from GPS Traces

Common goals of sustainable mobility approaches are to reduce the need for travel, to facilitate modal shifts, to decrease trip distances and to improve energy efficiency in the transportation systems. Among these issues, modal shift plays an important role for the adoption of vehicles with fewer or zero emissions. Nowadays, the electric bike (e-bike) is becoming a valid alternative to cars in urban areas. However, to promote modal shift, a better understanding of the mobility behaviour of e-bike users is required. In this paper, we investigate the mobility habits of e-bikers using GPS data collected in Belgium from 2014 to 2015. By analysing more than 10,000 trips, we provide insights about e-bike trip features such as: distance, duration and speed. In addition, we offer a deep look into which routes are preferred by bike owners in terms of their physical characteristics and how weather influences e-bike usage. Results show that trips with higher travel distances are performed during working days and are correlated with higher average speeds. Usage patterns extracted from our data set also indicate that e-bikes are preferred for commuting (home-work) and business (work related) trips rather than for recreational trips.

Detection of individual specimens in populations using contour energies

In this paper we study how shape information encoded in contour energy components values can be used for detection of microscopic organisms in population images. We proposed features based on shape and geometrical statistical data obtained from samples of optimized contour lines integrated in the framework of Bayesian inference for recognition of individual specimens. Compared with common geometric features the results show that patterns present in the image allow better detection of a considerable amount of individuals even in cluttered regions when sufficient shape information is retained. Therefore providing an alternative to building a specific shape model or imposing specific constrains on the interaction of overlapping objects.

Tracking multiple objects using moving snakes

Active contours or snakes are widely used for segmentation and tracking. The ability of a snake to track an object depends on the movement of the object. If the object moves too far from one frame to another, the snake risks losing the true contour location. The subsequent evolution steps are negatively affected, reporting a false contour that can propagate to other frames. To overcome this problem a new snake algorithm has been developed. This new technique, moving snakes, works in two steps. During the fist step, the snake is translated as a rigid body towards the contour. This translation is calculated using the external force field of the image, therefore it does not require prior knowledge about the object movement. In the second step the actual shape evolution of the snake takes place.

Geo-localized social media data to improve characterization of international travelers

The motivation for traveling across political borders might be influenced by the socio-economic condition and the educational level of the traveler. Ecuador is both a highly popular touristic destination and the home country of at least 1 million migrants. The relatively high availability and low cost of online social network data may be a complement to migration data in the study of travelers economical means and motivations. Such travel variables are relevant for countries as Ecuador in which the economy partly relies on the tourism industry and remittances. In this empirical study, we collected 63 millions of tweets, extracted the modal country for each user and studied the tweets generated. We first demonstrated, that the volume of local travelers leaving a country grouped by destination, as well as the volume of foreign travelers visiting the same country and grouped by provenance origin location, can be accurately estimated (r = 0.85) from geo-located records extracted from Twitter. Next, we characterized each group of travelers and computed spatial clusters, revealing popular locations of national and foreign travelers while in Ecuador. Finally, we showed that Ecuadorians with higher educational level have more chances to travel abroad, validating such findings with official data.

Hyperspectral imaging system for disease scanning on banana plants

Black Sigatoka (BS) is a banana plant disease caused by the fungus Mycosphaerella fijiensis. BS symptoms can be observed at late infection stages. By that time, BS has probably spread to other plants. In this paper, we present our current work on building an hyper-spectral (HS) imaging system aimed at in-vivo detection of BS pre-symptomatic responses in banana leaves. The proposed imaging system comprises a motorized stage, a high-sensitivity VIS-NIR camera and an optical spectrograph. To capture images of the banana leaf, the stages speed and cameras frame rate must be computed to reduce motion blur and to obtain the same resolution along both spatial dimensions of the resulting HS cube. Our continuous leaf scanning approach allows imaging leaves of arbitrary length with minimum frame loss. Once the images are captured, a denoising step is performed to improve HS image quality and spectral profile extraction.

Automatic identification of Caenorhabditis elegans in population images by shape energy features

Experiments on model organisms are used to extend the understanding of complex biological processes. In Caenorhabditis elegans studies, populations of specimens are sampled to measure certain morphological properties and a population is characterized based on statistics extracted from such samples. Automatic detection of C. elegans in such culture images is a difficult problem. The images are affected by clutter, overlap and image degradations. In this paper, we exploit shape and appearance differences between C. elegans and non‐C. elegans segmentations. Shape information is captured by optimizing a parametric open contour model on training data. Features derived from the contour energies are proposed as shape descriptors and integrated in a probabilistic framework. These descriptors are evaluated for C. elegans detection in culture images. Our experiments show that measurements extracted from these samples correlate well with ground truth data. These positive results indicate that the proposed approach can be used for quantitative analysis of complex nematode images.

Real-Time False-Contours Removal for Inverse Tone Mapped HDR Content

High Dynamic Ranges (HDR) displays can show images with higher color contrast levels and peak luminosities than the commonly used Low Dynamic Range (LDR) displays. Although HDR displays are still expensive, they are reaching the consumer market in the coming years. Unfortunately, most video content is recorded and/or graded in LDR format. Typically, dynamic range expansion by using an Inverse Tone Mapped Operator (iTMO) is required to show LDR content in HDR displays. The most common type of artifact derived from dynamic range expansion is false contouring, which negatively affects the overall image quality. In this paper, we propose a new fast iterative false-contour removal method for inverse tone mapped HDR content. We consider the false-contour removal as a signal reconstruction problem, and we solve it using an iterative Projection Onto Convex Sets (POCS) minimization algorithm. Unlike most other false-contour removal techniques, we define reconstruction constraints taking into account the iTMO used. Experimental results demonstrate the effectiveness of the proposed method to remove false contours while preserving details in the image. In order speed-up the execution time, the proposed method was implemented to run on a GPU. We were able to show that it can be used to remove false contours in real-time from an inverse tone mapped High-definition HDR video sequences at 24 fps.

Contour energy features for recognition of biological specimens in population images

In this paper we present an approach to perform automated analysis of nematodes in population images. Occlusion, shape variability and structural noise make reliable recognition of individuals a task difficult. Our approach relies on shape and geometrical statistical data obtained from samples of segmented lines. We study how shape similarity in the objects of interest, is encoded in active contour energy component values and exploit them to define shape features. Without having to build a specific model or making explicit assumptions on the interaction of overlapping objects, our results show that a considerable number of individual can be extracted even in highly cluttered regions when shape information is consistent with the patterns found in a given sample set.