Learning viewpoint planning in active recognition on a small sampling budget: a Kriging approach

International audienceThis paper focuses on viewpoint planning for 3D active object recognition. The objective is to design a planning policy into a Q-learning framework with a limited number of samples. Most existing stochastic techniques are therefore inapplicable. We propose to use Kriging and Bayesian Optimization coupled with Q-learning to obtain a computationally-efficient viewpoint-planning design, under a restrictive sampling budget. Experimental results on a representative database, including a comparison with classical approaches, show promising results for this strategy

Pseudogenization of a Sweet-Receptor Gene Accounts for Cats' Indifference toward Sugar

Although domestic cats (Felis silvestris catus) possess an otherwise functional sense of taste, they, unlike most mammals, do not prefer and may be unable to detect the sweetness of sugars. One possible explanation for this behavior is that cats lack the sensory system to taste sugars and therefore are indifferent to them. Drawing on work in mice, demonstrating that alleles of sweet-receptor genes predict low sugar intake, we examined the possibility that genes involved in the initial transduction of sweet perception might account for the indifference to sweet-tasting foods by cats. We characterized the sweet-receptor genes of domestic cats as well as those of other members of the Felidae family of obligate carnivores, tiger and cheetah. Because the mammalian sweet-taste receptor is formed by the dimerization of two proteins (T1R2 and T1R3; gene symbols Tas1r2 and Tas1r3), we identified and sequenced both genes in the cat by screening a feline genomic BAC library and by performing PCR with degenerate primers on cat genomic DNA. Gene expression was assessed by RT-PCR of taste tissue, in situ hybridization, and immunohistochemistry. The cat Tas1r3 gene shows high sequence similarity with functional Tas1r3 genes of other species. Message from Tas1r3 was detected by RT-PCR of taste tissue. In situ hybridization and immunohistochemical studies demonstrate that Tas1r3 is expressed, as expected, in taste buds. However, the cat Tas1r2 gene shows a 247-base pair microdeletion in exon 3 and stop codons in exons 4 and 6. There was no evidence of detectable mRNA from cat Tas1r2 by RT-PCR or in situ hybridization, and no evidence of protein expression by immunohistochemistry. Tas1r2 in tiger and cheetah and in six healthy adult domestic cats all show the similar deletion and stop codons. We conclude that cat Tas1r3 is an apparently functional and expressed receptor but that cat Tas1r2 is an unexpressed pseudogene. A functional sweet-taste receptor heteromer cannot form, and thus the cat lacks the receptor likely necessary for detection of sweet stimuli. This molecular change was very likely an important event in the evolution of the cat's carnivorous behavior

Active vision strategies for object recognition

Cette thèse, réalisée en coopération avec l’ONERA, concerne la reconnaissance active d’objets 3D par un agent autonome muni d’une caméra d’observation. Alors qu’en reconnaissance passive les modalités d’acquisitions des observations sont imposées et génèrent parfois des ambiguïtés, la reconnaissance active exploite la possibilité de contrôler en ligne ces modalités d’acquisition au cours d’un processus d’inférence séquentiel dans le but de lever l’ambiguïté. L’objectif des travaux est d’établir des stratégies de planification dans l’acquisition de l’information avec le souci d’une mise en œuvre réaliste de la reconnaissance active. Le cadre de l’apprentissage statistique est pour cela mis à profit. La première partie des travaux se consacre à apprendre à planifier. Deux contraintes réalistes sont prise en compte : d’une part, une modélisation imparfaite des objets susceptible de générer des ambiguïtés supplémentaires - d’autre part, le budget d’apprentissage est coûteux (en temps, en énergie), donc limité. La deuxième partie des travaux s’attache à exploiter au mieux les observations au cours de la reconnaissance. La possibilité d’une reconnaissance active multi-échelles est étudiée pour permettre une interprétation au plus tôt dans le processus séquentiel d’acquisition de l’information. Les observations sont également utilisées pour estimer la pose de l’objet de manière robuste afin d’assurer la cohérence entre les modalités planifiées et celles réellement atteintes par l’agent visuel.This PhD thesis, conducted in cooperation with ONERA, focuses on active 3D object recognition by an autonomous visual agent. Whereas in passive recognition, acquisition modalities of observations are fixed and may generate ambiguities, active recognition exploits the possibility of controling these modalities online in a sequential inference process in order to remove these ambiguities. The aim of this work is to design, in a statistical learning framework, planning strategies in the acquisition of information while achieving a realistic implementation of active recognition. The first part of the work is dedicated to learning to plan. Two realistic constraints are taken into account : on the one hand, planning with imperfect object modeling may generate further ambiguities - on the other hand, the learning cost (in time, energy) is expensive and therefore limited. The second part of this work focuses on maximally exploiting observations acquired during recognition. The possibility of an active multi-scale recognition is investigated to allow an interpretation as soon as the sequential acquisition process begins. Observations are also used to robustly estimate the pose of the object to ensure consistency between the planned and actual modality of the visual agent

Stratégies de vision active pour la reconnaissance d'objets

This PhD thesis, conducted in cooperation with ONERA, focuses on active 3D object recognition by an autonomous visual agent. Whereas in passive recognition, acquisition modalities of observations are fixed and may generate ambiguities, active recognition exploits the possibility of controling these modalities online in a sequential inference process in order to remove these ambiguities. The aim of this work is to design, in a statistical learning framework, planning strategies in the acquisition of information while achieving a realistic implementation of active recognition. The first part of the work is dedicated to learning to plan. Two realistic constraints are taken into account : on the one hand, planning with imperfect object modeling may generate further ambiguities - on the other hand, the learning cost (in time, energy) is expensive and therefore limited. The second part of this work focuses on maximally exploiting observations acquired during recognition. The possibility of an active multi-scale recognition is investigated to allow an interpretation as soon as the sequential acquisition process begins. Observations are also used to robustly estimate the pose of the object to ensure consistency between the planned and actual modality of the visual agent.Cette thèse, réalisée en coopération avec l’ONERA, concerne la reconnaissance active d’objets 3D par un agent autonome muni d’une caméra d’observation. Alors qu’en reconnaissance passive les modalités d’acquisitions des observations sont imposées et génèrent parfois des ambiguïtés, la reconnaissance active exploite la possibilité de contrôler en ligne ces modalités d’acquisition au cours d’un processus d’inférence séquentiel dans le but de lever l’ambiguïté. L’objectif des travaux est d’établir des stratégies de planification dans l’acquisition de l’information avec le souci d’une mise en œuvre réaliste de la reconnaissance active. Le cadre de l’apprentissage statistique est pour cela mis à profit. La première partie des travaux se consacre à apprendre à planifier. Deux contraintes réalistes sont prise en compte : d’une part, une modélisation imparfaite des objets susceptible de générer des ambiguïtés supplémentaires - d’autre part, le budget d’apprentissage est coûteux (en temps, en énergie), donc limité. La deuxième partie des travaux s’attache à exploiter au mieux les observations au cours de la reconnaissance. La possibilité d’une reconnaissance active multi-échelles est étudiée pour permettre une interprétation au plus tôt dans le processus séquentiel d’acquisition de l’information. Les observations sont également utilisées pour estimer la pose de l’objet de manière robuste afin d’assurer la cohérence entre les modalités planifiées et celles réellement atteintes par l’agent visuel

Stratégies de vision active pour la reconnaissance d'objets

Cette thèse, réalisée en coopération avec l ONERA, concerne la reconnaissance active d objets 3D par un agent autonome muni d une caméra d observation. Alors qu en reconnaissance passive les modalités d acquisitions des observations sont imposées et génèrent parfois des ambiguïtés, la reconnaissance active exploite la possibilité de contrôler en ligne ces modalités d acquisition au cours d un processus d inférence séquentiel dans le but de lever l ambiguïté. L objectif des travaux est d établir des stratégies de planification dans l acquisition de l information avec le souci d une mise en œuvre réaliste de la reconnaissance active. Le cadre de l apprentissage statistique est pour cela mis à profit. La première partie des travaux se consacre à apprendre à planifier. Deux contraintes réalistes sont prise en compte : d une part, une modélisation imparfaite des objets susceptible de générer des ambiguïtés supplémentaires - d autre part, le budget d apprentissage est coûteux (en temps, en énergie), donc limité. La deuxième partie des travaux s attache à exploiter au mieux les observations au cours de la reconnaissance. La possibilité d une reconnaissance active multi-échelles est étudiée pour permettre une interprétation au plus tôt dans le processus séquentiel d acquisition de l information. Les observations sont également utilisées pour estimer la pose de l objet de manière robuste afin d assurer la cohérence entre les modalités planifiées et celles réellement atteintes par l agent visuel.This PhD thesis, conducted in cooperation with ONERA, focuses on active 3D object recognition by an autonomous visual agent. Whereas in passive recognition, acquisition modalities of observations are fixed and may generate ambiguities, active recognition exploits the possibility of controling these modalities online in a sequential inference process in order to remove these ambiguities. The aim of this work is to design, in a statistical learning framework, planning strategies in the acquisition of information while achieving a realistic implementation of active recognition. The first part of the work is dedicated to learning to plan. Two realistic constraints are taken into account : on the one hand, planning with imperfect object modeling may generate further ambiguities - on the other hand, the learning cost (in time, energy) is expensive and therefore limited. The second part of this work focuses on maximally exploiting observations acquired during recognition. The possibility of an active multi-scale recognition is investigated to allow an interpretation as soon as the sequential acquisition process begins. Observations are also used to robustly estimate the pose of the object to ensure consistency between the planned and actual modality of the visual agent.CACHAN-ENS (940162301) / SudocSudocFranceF

Alignment of Deduced Amino Acid Sequences of T1R3 and T1R2 from Five Species

<p>This figure shows the alignment of the deduced sequences of the taste receptor proteins, T1R3 and T1R2, from domestic cat, domestic dog, human, mouse, and rat. Amino acids that are identical among species are shaded in black; conservative amino acid substitutions are shaded in gray. The cat T1R3 sequence shows high similarity with that of human and rodents, with especially high similarity with that of dog. The predicted cat T1R2 sequence is truncated at amino acid 355 due to a premature stop codon at bp 57–59 in exon 4, which results from a 247-bp deletion in exon 3. The underlined amino acids from 316 to 355 of the cat T1R2 result from the frame shift brought by the 247-bp deletion in exon 3. Note that the deduced amino acid sequence of dog T1R2 predicts an apparently normal protein showing high similarity with that of rat, mouse, and human.</p

Protein Expression of Cat T1R2 and T1R3

<p>Cat T1R3 expression is detected in taste buds of circumvallate papilla (CV) (A) and a fungiform papilla (Fun) just anterior to the intermolar eminence (B) by labeling with anti-mouse T1R3 antibody. Cat T1R2 expression is not detectable in either circumvallate (C) or fungiform (D) using an anti-cat T1R2 antibody. Control studies demonstrated that the anti-cat T1R2 antibody labeled a subset of taste bud cells in rat circumvallate (data not shown). Scale bar, shown only in panel (A) and (B), = 60 μm for (A) and = 45 μm for (B). Scale for panel (C) is the same as that of panel (A); scale for panel (D) is the same as that of panel (B).</p