Blind Vision

Avidan, Shai; Butman, Moshe

doi:10.1007/11744078_1

Cited by 79 publications

(75 citation statements)

References 10 publications

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“…Content based Image Retrieval techniques are found in [1]. Both image operations and cryptographic primitives are combinely studied in [12], [13]. In [4], [14], the authors study about the oblivious/private retrieval of images.…”

Section: A Limitation Of Prior Researchmentioning

confidence: 99%

An Oblivious Image Retrieval Protocol

Sabbu

Umadevi

Srinathan

et al. 2011

2011 IEEE Workshops of International Conference on Advanced Information Networking and Applications

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Abstract-We consider the novel problem of oblivious retrieval of images from an outsourced database server. Due to the complexity of managing images, in modern enterprise networks, individual users and organizations are using the services of external servers to store the images. The problem is to query and retrieve images while simultaneously satisfying the criteria of both the user and the database privacy. Oblivious transfer protocols are capable of ensuring the privacy of both the user and the server privacy at the same time. But in their native form, oblivious transfer protocols may not be suitable for querying and retrieving images due to the large size of the images. In this paper, we describe the first such attempt to develop an oblivious retrieval protocol that can achieve the goals of user and database privacy. which allows the user to query for the images of interest. In this phase, the database server does not know the exact user query and the user does not learn of any other images in the database. In the next phase, the user deploys an oblivious transfer protocol to retrieve the necessary decryption for the selected images without revealing the actual set of decryption keys selected. We have evaluated our protocol using an experimental setting and show that the results are a reasonable trade-off when compared to the security requirements being satisfied. We have implemented a working prototype and tested the protocol on a database of 9000 large real-life images.

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Section: A Limitation Of Prior Researchmentioning

confidence: 99%

An Oblivious Image Retrieval Protocol

Sabbu

Umadevi

Srinathan

et al. 2011

2011 IEEE Workshops of International Conference on Advanced Information Networking and Applications

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show abstract

“…To find the coprime kernels in a 2D CBP, we first uniformly sample the polynomials in the first dimension (z 1 ) on the unit circle within the complex domain. At each sample point z 1 …”

Section: Compute Kernel K1(z) According To Eqn(10) 5 Compute Kernementioning

confidence: 99%

“…Therefore, our methods are much more efficient than previous methods based on recovering the latent image through GCDs [19]. For instance, we only need to evaluate the polynomials b 1 it takes O(n 2 log(n)) operations for FFT division. Hence, the overall complexity of our algorithm is O(n 2 log(n)) for recovering both the latent image and blur kernels in a CBP of image resolution n × n. Table. 1 shows the processing speed of our CBP-based deblurring algorithm at different image and kernel resolutions.…”

Section: Compute Kernel K1 By Applying Inverse Fft Tomentioning

confidence: 99%

“…For example, by measuring the coprimality between the blur images, we can easily estimate the size of the kernel and hence simplify previous guess-and-check approaches. We further explore applying the CBP theory for conducting privacy-preserving surveillance, a field that has attracted an increasing amount of attention in recent years [1,26]. Our solution is to provide multi-level video surveillance streams by strategically blurring the video contents.…”

Section: Introductionmentioning

confidence: 99%

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A theory of Coprime Blurred Pairs

Saunders

et al. 2011

2011 International Conference on Computer Vision

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“…An affective computing tool is the development of a system, which is able to recognize, interpret, and simulate human emotions. Our affective computing tool is built upon existing research [12,13,14,15,16,17,18]. Linking two modalities (face expression and voice intonation) into a single system for affective computing analysis is not new and has been studied before [19,20,21,22,23,24].…”

Section: Introductionmentioning

confidence: 99%

FILTWAM and Voice Emotion Recognition

Bahreini

Nadolski

Westera

2014

Lecture Notes in Computer Science

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Abstract. This paper introduces the voice emotion recognition part of our framework for improving learning through webcams and microphones (FILTWAM). This framework enables multimodal emotion recognition of learners during game-based learning. The main goal of this study is to validate the use of microphone data for a real-time and adequate interpretation of vocal expressions into emotional states were the software is calibrated with end users. FILTWAM already incorporates a valid face emotion recognition module and is extended with a voice emotion recognition module. This extension aims to provide relevant and timely feedback based upon learner's vocal intonations. The feedback is expected to enhance learner's awareness of his or her own behavior. Six test persons received the same computer-based tasks in which they were requested to mimic specific vocal expressions. Each test person mimicked 82 emotions, which led to a dataset of 492 emotions. All sessions were recorded on video. An overall accuracy of our software based on the requested emotions and the recognized emotions is a pretty good 74.6% for the emotions happy and neutral emotions; but will be improved for the lower values of an extended set of emotions. In contrast with existing software our solution allows to continuously and unobtrusively monitor learners' intonations and convert these intonations into emotional states. This paves the way for enhancing the quality and efficacy of game-based learning by including the learner's emotional states, and links these to pedagogical scaffolding.

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Blind Vision

Cited by 79 publications

References 10 publications

An Oblivious Image Retrieval Protocol

An Oblivious Image Retrieval Protocol

A theory of Coprime Blurred Pairs

FILTWAM and Voice Emotion Recognition

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