Recovering Faces from Portraits with Auxiliary Facial Attributes

Recovering a photorealistic face from an artistic portrait is a challenging task since crucial facial details are often distorted or completely lost in artistic compositions. To handle this loss, we propose an Attribute-guided Face Recovery from Portraits (AFRP) that utilizes a Face Recovery Network (FRN) and a Discriminative Network (DN). FRN consists of an autoencoder with residual block-embedded skip-connections and incorporates facial attribute vectors into the feature maps of input portraits at the bottleneck of the autoencoder. DN has multiple convolutional and fully-connected layers, and its role is to enforce FRN to generate authentic face images with corresponding facial attributes dictated by the input attribute vectors. %Leveraging on the spatial transformer networks, FRN automatically compensates for misalignments of portraits. % and generates aligned face images. For the preservation of identities, we impose the recovered and ground-truth faces to share similar visual features. Specifically, DN determines whether the recovered image looks like a real face and checks if the facial attributes extracted from the recovered image are consistent with given attributes. %Our method can recover high-quality photorealistic faces from unaligned portraits while preserving the identity of the face images as well as it can reconstruct a photorealistic face image with a desired set of attributes. Our method can recover photorealistic identity-preserving faces with desired attributes from unseen stylized portraits, artistic paintings, and hand-drawn sketches. On large-scale synthesized and sketch datasets, we demonstrate that our face recovery method achieves state-of-the-art results.Comment: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV

Production of Denatured Whey Protein Concentrate at Various pHfrom Wastewater of Cheese Industry

Wastewater produced from cheese industry is rich in biological component such as whey protein, fat and lactose. Whey protein is the residual liquid of cheese making process with a high protein efficiency ratio. The wastewater source used in this study was whey liquid from cheese processing industry located at West Java, Indonesia. Conversion of soluble whey protein into whey protein microparticle is required to produce food with nutritional value that can be adjusted to the needs of the specific target with high digestibility and palatability. Whey protein was collected by separation technique through heat treatment at specific condition. This was done by changing the heat treatment condition and pH of the samples. Changing the pH of the samples before heat treatment affect the ionic strength of the whey protein hence, altering the properties of the concentrate. This study aims to produce whey protein concentrate heated at various pH level and to observe physicochemical and functional properties of the concentrates. The method used in this research was a descriptive method conducted on three treatments and two replications namely whey protein concentrate production in a pH condition 6.4; 6.65; and 7.0. The parameters observed were physicochemical and functional properties. Furthermore, the result showed that there were decrease in protein content, along with the increasing pH before heat treatment. Microstructure image (SEM) showed a finer particles with the increasing pH. Meanwhile, solubility of the rehydrated samples tends to increase along with the increasing pH. The measurement of functional properties of the samples showed that denatured whey protein produced at different pH before heat treatment have different water holding capacity and a tendency to form bonds between protein particles thereby increasing the viscosity value. These physicochemical and functional properties were suitable for denatured whey protein to be used as a texture controller in whey protein based-food production

Face Hallucination via Deep Neural Networks.

We firstly address aligned low-resolution (LR) face images (i.e. 16X16 pixels) by designing a discriminative generative network, named URDGN. URDGN is composed of two networks: a generative model and a discriminative model. We introduce a pixel-wise L2 regularization term to the generative model and exploit the feedback of the discriminative network to make the upsampled face images more similar to real ones. We present an end-to-end transformative discriminative neural network (TDN) devised for super-resolving unaligned tiny face images. TDN embeds spatial transformation layers to enforce local receptive fields to line-up with similar spatial supports. To upsample noisy unaligned LR face images, we propose decoder-encoder-decoder networks. A transformative discriminative decoder network is employed to upsample and denoise LR inputs simultaneously. Then we project the intermediate HR faces to aligned and noise-free LR faces by a transformative encoder network. Finally, high-quality hallucinated HR images are generated by our second decoder. Furthermore, we present an end-to-end multiscale transformative discriminative neural network (MTDN) to super-resolve unaligned LR face images of different resolutions in a unified framework. We propose a method that explicitly incorporates structural information of faces into the face super-resolution process by using a multi-task convolutional neural network (CNN). Our method not only uses low-level information (i.e. intensity similarity), but also middle-level information (i.e. face structure) to further explore spatial constraints of facial components from LR inputs images. We demonstrate that supplementing residual images or feature maps with additional facial attribute information can significantly reduce the ambiguity in face super-resolution. To explore this idea, we develop an attribute-embedded upsampling network. In this manner, our method is able to super-resolve LR faces by a large upscaling factor while reducing the uncertainty of one-to-many mappings remarkably. We further push the boundaries of hallucinating a tiny, non-frontal face image to understand how much of this is possible by leveraging the availability of large datasets and deep networks. To this end, we introduce a novel Transformative Adversarial Neural Network (TANN) to jointly frontalize very LR out-of-plane rotated face images (including profile views) and aggressively super-resolve them by 8X, regardless of their original poses and without using any 3D information. Besides recovering an HR face images from an LR version, this thesis also addresses the task of restoring realistic faces from stylized portrait images, which can also be regarded as face hallucination

I Simulatori in realtà virtuale: un ausilio nella formazione chirurgica

Negli ultimi anni la necessità di formazione in campo laparoscopico ha spinto verso la creazione di simulatori chirurgici di diversa fattura e diversa complessità. Al momento molti di questi sono disponibili in commercio. Ognuna di questi ha il proprio design, struttura e programma di formazione. L'evoluzione è rappresentata dall’utilizzo della Realtà Virtuale, che mima l'azione reale e lavora sulle diverse competenze acquisite durante i corsi di formazione e l’esperienza chirurgica al campo operatorio. Il ruolo della formazione "sicura ed efficiente" è necessario nel corso di una specializzazione in chirurgia e durante la formazione continua. La simulazione in realtà virtuale è in grado di offrire un numero infinito di scenari chirurgici. I simulatori chirurgici in realtà virtuale di ultima generazione sono forniti di percorsi di formazione graduali che guidano lo specializzando nell’acquisizione di manualità “fine” nei singoli tasks fino alla procedura completa “full task” di un intervento chirurgico, ad esempio una colecistectomia. In questo studio abbiamo voluto testare la validità di un’acquisizione graduale di tecnica manuale “step by step” rispetto all’esercizio diretto solo su una procedura completa mediante l’ausilio di un simulatore in Virtual Reality, il LapMentor®(Simbionix,Israele). Specializzandi in Chirurgia Generale privi di esperienza precedente in laparoscopia hanno ottenuto risultati migliori sulla procedura completa della colecistectomia laparoscopica procedendo durante il corso step by step rispetto a coloro che hanno eseguito la procedura completa “full task” direttamente. Il nostro studio conferma che una buona esperienza e la conoscenza delle capacità tecniche di base nel campo della formazione laparoscopica migliorano le prestazioni nella procedura completa.In the last years the need for training in laparoscopy has led to the creation of surgical simulators of varying complexity and different bill. Currently, many of these are commercially available. Each of these has its own design, structure and training program. The trend is the use of virtual reality, which mimics the real action and work on various skills acquired during the training and experience in the surgical operating field. The role of training on safe and efficient "is necessary in the course of specialization in surgery and during the training. The simulation in virtual reality is able to offer an infinite number of surgical scenarios. The surgical simulators in virtual reality are equipped with the latest training courses that guide the gradual specializing in the acquisition of manual skills "end" in the individual tasks to complete procedure "full task" for surgery, such as a cholecystectomy. In this study we wanted to test the validity of the gradual acquisition of technical manual "step by step" only on a direct comparison with the whole procedure with the help of a mortgage in Virtual Reality, the LapMentor ® (Simbionix, Israel) .Specializing in general surgery with no previous experience in laparoscopy have performed better on the whole procedure of laparoscopic cholecystectomy during the course of proceeding step by step than those who performed the procedure complete "full task" directly. Our study confirms that a good experience and knowledge of basic technical skills in training laparoscopic improve performance in the whole procedure

A texture controller

We propose an efficient method for creating accurate and controllable textures. In the past, procedural textures have been used to cover every point of an object uniformly, although their behaviour could not be controlled locally, as is frequently needed. In order to provide local control, texture-attribute control points are inserted in the model, and the behaviour of the texture at every point is defined through interpolation of the control-point attributes. The texturing algorithm proposed behaves as a texture controller and can be applied to any kind of procedural texture

Production of Denatured Whey Protein Concentrate at Various PHfrom Wastewater of Cheese Industry

Wastewater produced from cheese industry is rich in biological component such as whey protein, fat and lactose. Whey protein is the residual liquid of cheese making process with a high protein efficiency ratio. The wastewater source used in this study was whey liquid from cheese processing industry located at West Java, Indonesia. Conversion of soluble whey protein into whey protein microparticle is required to produce food with nutritional value that can be adjusted to the needs of the specific target with high digestibility and palatability. Whey protein was collected by separation technique through heat treatment at specific condition. This was done by changing the heat treatment condition and pH of the samples. Changing the pH of the samples before heat treatment affect the ionic strength of the whey protein hence, altering the properties of the concentrate. This study aims to produce whey protein concentrate heated at various pH level and to observe physicochemical and functional properties of the concentrates. The method used in this research was a descriptive method conducted on three treatments and two replications namely whey protein concentrate production in a pH condition 6.4; 6.65; and 7.0. The parameters observed were physicochemical and functional properties. Furthermore, the result showed that there were decrease in protein content, along with the increasing pH before heat treatment. Microstructure image (SEM) showed a finer particles with the increasing pH. Meanwhile, solubility of the rehydrated samples tends to increase along with the increasing pH. The measurement of functional properties of the samples showed that denatured whey protein produced at different pH before heat treatment have different water holding capacity and a tendency to form bonds between protein particles thereby increasing the viscosity value. These physicochemical and functional properties were suitable for denatured whey protein to be used as a texture controller in whey protein based-food production