關於 image signal process ，我們在網路上蒐集到這些相關的討論、資訊與評價

有興趣加入DAINESE HK的車手們，機會來啦！
快啲將你履歷等資料send來比 Dainese Hong Kong 啦~
https://hk.jobsdb.com/....../senior-sales-associate......

RESPONSIBILITIES
Welcome customers to the store and answer their enquiries
負責店舖內銷售工作及解答客人問題

Work as a team to achieve sales targets
團隊合作，實現銷售目標

Maintain store image through changing design and displaying new arrival products in store
裝飾櫥窗佈置及整理陳列商品

Closely coordinate with logistic team to process new arrival shipments and comply with inventory control procedures
跟進貨品倉存，來貨及轉貨點算

Help daily shop operations
協助店舖的運作

Assist in marketing activities as required
參與市場推廣活動

REQUIREMENTS
F.5 or above
At least 3 years solid experience in sales role, especially in retail sales
3年或以上銷售工作經驗，具零售經驗為佳

Good command of Cantonese, Mandarin and English
良好粵語、英語、及普通話

Team player with strong communication skill
具團隊精神及有良好的溝通技巧

Mature, independent, self-motivated and proactive personality
成熟、獨立、熱誠有禮，開朗積極

Results driven and team-oriented with positive and engaging attitude
以積極和參與的態度推動結果和團隊導向

OUR OFFER
At least 14 days Annual Leave
Birthday Leave
Discretionary Bonus
Employer MPF Contribution 6%
Life & Medical Insurance Marriage Leave and Paternity Leave
Training and Development Programs
Employee Wellness Programs and Staff Discount
__________________________________________
DAINESE HONG KONG
香港北角七姊妹道196號地下
G/F, 196 Tsat Tsz Mui Road, North Point, Hong Kong
港鐵鰂魚涌站C出口
MTR QUARRY BAY Station Exit C
查詢電話: 2564 6622
Whatsapp: 9628 4720(MSG only)
Signal: 9628 4720(MSG only)
Facebook: Dainese Hong Kong
Mewe: Dainese Hong Kong

เดี๋ยวนะเพิ่งอธิบายเรื่อง "สีรองเท้า" นี้ไปวันก่อนนี่เอง ทำไมมันกลายเป็นเรื่อง "การใช้สมองซีกซ้ายซีกขวา" ไปแล้วล่ะ.... ไม่เกี่ยวกันนะครับ ! จริงๆ มันเป็นแค่เรื่องของสมองเราปรับค่าแสงตามสภาพแวดล้อมเท่านั้นเอง แล้วแต่ละคนก็มีการปรับค่าแตกต่างกันไป ไม่ใช่เรื่องการใช้สมองซีกซ้ายซีกขวา เป็นศิลปิน หรือเป็นโลจิกอะไร
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(เนื้อหาที่เขียนอธิบายไปก่อนหน้านี้)

"คุณเห็นรองเท้านี้เป็นสีอะไร ชมพูคาดขาว หรือ เทาคาดเขียว"
...Continue ReadingWait. I just explained this " Shoe Color " the other day. Why has it become " using the left and right brain.... it's not related! In fact, it's just a matter of our brain. We adjust the light cost according to the environment. and each person has different values. It's not about using the left brain, right, right as an artist or what is it?
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(previously written content)

" what color do you see this shoe? Pink, white or green grey

Become a global hype with puzzle - ask what color after last year, there are many photos of the long dress to argue about blue-Black or white-Gold... this time there are many sneakers. People (including hair) think it's Grey, expected with green stripes. While many others say no, no, no, no, it's pink and it's white lines... so what color is this?

The answer is pink-White. Oh... this photo starts with a girl named Nicole Coulthard posted a photo of shoes on fb girlsmouth because her friend bought shoes and took a photo and sent it to mom. Mother doesn't see pink, but she sees it as grey, green, blue?!

What happened to this mystery shoe... if we put them into a program like photoshop to check the color, the answer is grey-Green? .. but if you notice, look good at the hand of those who hold the shoe, you will see that the color of the hand is crazy. Go out to green... and if anyone tries to adjust the color of your hand to natural color, the shoes will be pink. In fact again

A summary of this story that the color of Pink-White to Grey-Green is a result of a bad light condition from using flash in the dark, including the quality of the camera.

But eh, why is someone who can see pink right? ... this can explain like the case of dress as follows.

Our eyes have a tissue in the back called Retina (Retina), which contains aperture (Photoreceptor). The Aperspecs will signal through the brain to the brain to interpret the picture or image we see. There are 2 types of aperture. Rod and cone (Cone). The Rod). The Rod will receive a night-time image of grey and brightness... while cone cells will receive colorful daytime images by Cone cells are divided into 3 different wavelengths, or different colors are getting green, Red, Blue, which our brains will evaluate what color the image is from the mix of signs. COMING FROM ALL 3 of these cells

Therefore, from the process of the cone-receiving cell signals in each person, there are different. The Photos taken are " Low brightness " and " a glimpse of color " like this shoe or dress. Earlier, each person's brain processed the image differently.

If someone's brain interprets enough light, it's likely to look at the shoes as grey, Green (or blue-Black dress) while anyone's brain interprets this image is too dark, it's likely. Look at that picture, compensate for more "Light" see the shoes in pink, White (or white-Gold Dress)

While this picture has a glimpse in the green sky from the beginning, the brain of the censitev and the picture with a glimpse will try to compensate "Color" to be less blue. Fine, it will come out more pink.

Check it out from the last picture that is blue stripes. On the other colors. On Top of the picture, we often see the blue stripes brighter than usual. As the bottom of the picture, we often see darker blue stripes are darker, even though the blue bar is actually color. All the same picture is the same.

Info from https://www.buzz.ie/news/dress-2-0-people-cant-decide-colour-trainers-257450
And http://www.businessinsider.com/what-color-are-the-shoes-pink-white-teal-gray-2017-10 and http://www.businessinsider.com/science-of-the-white-and-gold-blue-and-black-dress-illusion-2015-2Translated

感謝東吳巨量資料學院的胡學長，貢獻了大數據英文用語的part 2囉！
這次胡學長focus在「機器學習篇」，以及介紹三個因為機器學習出現而發展非常快速的領域，分別是「文字」、「影像」和「音訊」。
大家不用覺得機器學習離我們很遙遠，像是youtube的推薦系統、google翻譯以及siri的背後通通都是使用機器學習的演算法哦！而且其實概念並不難，有興趣的孩子可以多探究！
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🤖 胡哥+俐媽英文教室—機器學習篇 machine learning：
1️⃣ <機器學習四大類別>
* supervised learning 監督式學習
* semi-supervised learning 半監督式學習
* unsupervised learning 非監督式學習
* reinforcement learning 強化學習

2️⃣ <常見用語>
* cluster 分群
* classification 分類
* regression 迴歸
* model 模型
* parameter 參數
* predict 預測
* accuracy 準確率
* overfitting 過度擬合
* feature 特徵欄位
* label 目標欄位
* training data 訓練資料
* testing data 測試資料
* validation data 驗證資料
* standardization 資料標準化
* feature extraction 特徵提取
* dimension reduction 資料降維

3️⃣ <文字分析>
* text mining 文字探勘
* natural language process 自然語言處理
* text categorization 文本分類
* information retrieval 資訊檢索
* word segmentation 自動分詞
* machine translation 機器翻譯
* topic modeling 主題式分析
* sentiment analysis 文字情緒分析
* part of speech 文字詞性分析
* word embedding 詞向量轉換

4️⃣ <影像辨識>
* computer vision 電腦視覺
* image recognition 影像辨識
* image segmentation 影像切割
* image annotation 影像標注
* object detection 物件偵測
* face detection 人臉辨識

5️⃣ <音訊辨識>
* speech recognition 語音辨識
* signal extraction 訊號處理
* noise reduction 雜訊處理
* acoustic model 聲學模型
* time domain 時域
* frequency domain 頻域
* Fourier transform 傅立葉轉換
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真是隔行如隔山，有你們提供其他專業領域英文，大家都彼此受惠!
感謝胡哥🙏🏼～
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#俐媽英文教室 #俐媽英文教室徵稿中 #俐媽英文教室大數據篇 #謝謝胡哥 #東吳巨量資料學院 #大數據 #bigdata #AI #machinelearning