Our leather sandals are special and different because:

- The sandals are made of premium Italian vegetative processed leather.

- Our leather is colored only with natural extracts from plants by ancient recipes in Tuscany (Italy), and not like the majority of sandals from cheap chemically treated leather. Remember that the sandals are shod on bare feet!

- They are handmade. Not mass production.

- The leather is very soft and will not ulcerate your feet and at the same time does not stretch.

- Extremely durable. These sandals are designed for all-day walks. You will wear them the next 3-4 years.

- The sandals have a quality certificate, we have the authority to use the logo "Real Italian leather." Only 7 workshops for processing of leather in the world can provide such authority.

- The warranty for all of our products is one calendar year.

- They are handmade with lots of love and effort especially for you :)

-------------------------------------

SIZING FOR THIS MODEL:

Inside soles measurements:

US5 | EU 35 - 8.9 inch / 22.7 cm

US6 | EU 36 - 9.2 inch / 23.4 cm

US6.5 | EU 37 - 9.5 inch / 24.1 cm

US7.5 | EU 38 - 9.7 inch / 24.7 cm

US8.5 | EU 39 - 10 inch / 25.4 cm

US9 | EU 40 - 10.3 inch / 26.1 cm

US9.5 | EU 41 - 10.5 inch / 26.7 cm

How to measure?

1/Please step on a piece of paper and outline your foot.

2/Now measure the longest distance (from heel to tip of toes).

3/Check the table above to see where you stand.

-------------------------------------

ENVIRONMENT:

OUR SANDALS ARE IN COMPLETE HARMONY WITH THE ENVIRONMENT!

The vegetative treatment is a process born in the nature. The vegetative processed leathers contain no toxic substances such as azo dyes, nickel, pentachlorophenol, chromium VI, all substances that are harmful to humans and the environment. These substances are the main causes of a number of allergies. Due to the absence of heavy metals and the presence only of natural extracts, the leather is in perfect harmony with the environment.

Additional information:

- The technological process is subject to strict continuous monitoring, with which to ensure the low impact on the human the environment and the right natural selection of the materials. This control is performed by the “Italian Society of Allergological, Environmental and Occupational Dermatology

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Browse Conferences > 2014 IEEE Conference on Compu... Summer Sandals Summer Sandals Shoes Slingback Leather Closed Flats Sandals Sandals Sandals Toe Casual Women Summer Eggplant

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An improved image segmentation algorithm for salient object detection

Object detection and segmentation on a hierarchical region-based image represent...

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Abstract: Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that typically combine multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3%. Our approach combines two key insights:... View more

Metadata

Published in

2014 IEEE Conference on Computer Vision and Pattern Recognition

Date of Conference

23-28 June 2014

Date Added to IEEE Xplore

25 September 2014

Electronic ISBN: 978-1-4799-5118-5

ISSN Information

INSPEC Accession Number

14632381

Sandals Casual Leather Sandals Summer Sandals Summer Sandals Sandals Summer Closed Slingback Women Flats Shoes Eggplant Toe DOI

10.1109/CVPR.2014.81

Publisher

IEEE

Conference Location

Columbus, OH, USA

Abstract:

Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that typically combine multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3%. Our approach combines two key insights: (1) one can apply high-capacity convolutional neural networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost. Since we combine region proposals with CNNs, we call our method R-CNN: Regions with CNN features. We also present experiments that provide insight into what the network learns, revealing a rich hierarchy of image features. Source code for the complete system is available at http://www.cs.berkeley.edu/~rbg/rcnn.

Published in: 2014 IEEE Conference on Computer Vision and Pattern Recognition

Date of Conference: 23-28 June 2014

Date Added to IEEE Xplore: 25 September 2014

Electronic ISBN: 978-1-4799-5118-5

ISSN Information:

INSPEC Accession Number: 14632381

DOI: 10.1109/CVPR.2014.81

Publisher: IEEE

Conference Location: Columbus, OH, USA

Contents

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1. Introduction

Features matter. The last decade of progress on various visual recognition tasks has been based considerably on the use of SIFT [27] and HOG [7]. But if we look at performance on the canonical visual recognition task, PASCAL VOC object detection [13], it is generally acknowledged that progress has been slow during 2010–2012, with small gains obtained by building ensemble systems and employing minor variants of successful methods.

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