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According to film mythology, the Soviet filmmaker Lev Kuleshov conducted an experiment in which he combined a close-up of an actor’s neutral face with three different emotional contexts: happiness, sadness, and hunger. The viewers of the three film sequences reportedly perceived the actor’s face as expressing an emotion congruent with the given context. It is not clear, however, whether or not the so-called “Kuleshov effect” really exists. The original film footage is lost and recent attempts at replication have produced either conflicting or unreliable results. The current paper describes an attempt to replicate Kuleshov’s original experiment using an improved experimental design. In a behavioral and eye tracking study, 36 participants were each presented with 24 film sequences of neutral faces across six emotional conditions. For each film sequence, the participants were asked to evaluate the emotion of the target person in terms of valence, arousal, and category. The participants’ eye movements were recorded throughout. The results suggest that some sort of Kuleshov effect does in fact exist. For each emotional condition, the participants tended to choose the appropriate category more frequently than the alternative options, while the answers to the valence and arousal questions also went in the expected directions. The eye tracking data showed how the participants attended to different regions of the target person’s face (in light of the intermediate context), but did not reveal the expected differences between the emotional conditions. [ABSTRACT FROM AUTHOR]

Highlights: [•] We argue for the need to compare semiotic vehicles and ostensive cues in object-choice studies. [•] We describe such a study with 3 age-groups of children, estimating vocabulary. [•] We compare with 4 chimpanzees, including the variable “familiar/unfamiliar helper”. [•] Pointing/Marker was understood better than Picture/Replica. [•] A similar tendency for chimpanzees, but no group effect or for helper-familiarity. [Copyright &y& Elsevier]

Edge-based color constancy methods make use of image derivatives to estimate the illuminant. However, different edge types exist in real-world images, such as material, shadow, and highlight edges. These different edge types may have a distinctive influence on the performance of the illuminant estimation. Therefore, in this paper, an extensive analysis is provided of different edge types on the performance of edge-based color constancy methods. First, an edge-based taxonomy is presented classifying edge types based on their photometric properties (e.g., material, shadow-geometry, and highlights). Then, a performance evaluation of edge-based color constancy is provided using these different edge types. From this performance evaluation, it is derived that specular and shadow edge types are more valuable than material edges for the estimation of the illuminant. To this end, the (iterative) weighted Gray-Edge algorithm is proposed in which these edge types are more emphasized for the estimation of the illuminant. Images that are recorded under controlled circumstances demonstrate that the proposed iterative weighted Gray-Edge algorithm based on highlights reduces the median angular error with approximately 25 percent. In an uncontrolled environment, improvements in angular error up to 11 percent are obtained with respect to regular edge-based color constancy. [ABSTRACT FROM AUTHOR]

The segmentation of a single material reflectance is a challenging problem due to the considerable variation in image measurements caused by the geometry of the object, shadows, and specularities. The combination of these effects has been modeled by the dichromatic reflection model. However, the application of the model to real-world images is limited due to unknown acquisition parameters and compression artifacts. In this paper, we present a robust model for the shape of a single material reflectance in histogram space. The method is based on a multilocal creaseness analysis of the histogram which results in a set of ridges representing the material reflectances. The segmentation method derived from these ridges is robust to both shadow, shading and specularities, and texture in real-world images. We further complete the method by incorporating prior knowledge from image statistics, and incorporate spatial coherence by using multiscale color contrast information. Results obtained show that our method clearly outperforms state-of-the-art segmentation methods on a widely used segmentation benchmark, having as a main characteristic its excellent performance in the presence of shadows and highlights at low computational cost. [ABSTRACT FROM PUBLISHER]

Blur detection aims at segmenting the blurred areas of a given image. Recent deep learning-based methods approach this problem by learning an end-to-end mapping between the blurred input and a binary mask representing the localization of its blurred areas. Nevertheless, the effectiveness of such deep models is limited due to the scarcity of datasets annotated in terms of blur segmentation, as blur annotation is labor intensive. In this work, we bypass the need for such annotated datasets for end-to-end learning, and instead rely on object proposals and a model for blur generation in order to produce a dataset of synthetically blurred images. This allows us to perform self-supervised learning over the generated image and ground truth blur mask pairs using CNNs, defining a framework that can be employed in purely self-supervised, weakly supervised or semi-supervised configurations. Interestingly, experimental results of such setups over the largest blur segmentation datasets available show that this approach achieves state of the art results in blur segmentation, even without ever observing any real blurred image. [ABSTRACT FROM AUTHOR]